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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

Identify the Problem
Define the Problem
Form a Strategy
Organize Information
Allocate Resources
Monitor Progress
Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

Asking questions about the problem
Breaking the problem down into smaller pieces
Looking at the problem from different perspectives
Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

Get Advice From The Verywell Mind Podcast

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You can become a better problem solving by:

Practicing brainstorming and coming up with multiple potential solutions to problems
Being open-minded and considering all possible options before making a decision
Breaking down problems into smaller, more manageable pieces
Asking for help when needed
Researching different problem-solving techniques and trying out new ones
Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors. The Psychology of Problem Solving . Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving . Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Center for Teaching

Teaching problem solving.

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Tips and Techniques

Expert vs. novice problem solvers, communicate.

Have students identify specific problems, difficulties, or confusions . Don’t waste time working through problems that students already understand.
If students are unable to articulate their concerns, determine where they are having trouble by asking them to identify the specific concepts or principles associated with the problem.
In a one-on-one tutoring session, ask the student to work his/her problem out loud . This slows down the thinking process, making it more accurate and allowing you to access understanding.
When working with larger groups you can ask students to provide a written “two-column solution.” Have students write up their solution to a problem by putting all their calculations in one column and all of their reasoning (in complete sentences) in the other column. This helps them to think critically about their own problem solving and helps you to more easily identify where they may be having problems. Two-Column Solution (Math) Two-Column Solution (Physics)

Encourage Independence

Model the problem solving process rather than just giving students the answer. As you work through the problem, consider how a novice might struggle with the concepts and make your thinking clear
Have students work through problems on their own. Ask directing questions or give helpful suggestions, but provide only minimal assistance and only when needed to overcome obstacles.
Don’t fear group work ! Students can frequently help each other, and talking about a problem helps them think more critically about the steps needed to solve the problem. Additionally, group work helps students realize that problems often have multiple solution strategies, some that might be more effective than others

Be sensitive

Frequently, when working problems, students are unsure of themselves. This lack of confidence may hamper their learning. It is important to recognize this when students come to us for help, and to give each student some feeling of mastery. Do this by providing positive reinforcement to let students know when they have mastered a new concept or skill.

Encourage Thoroughness and Patience

Try to communicate that the process is more important than the answer so that the student learns that it is OK to not have an instant solution. This is learned through your acceptance of his/her pace of doing things, through your refusal to let anxiety pressure you into giving the right answer, and through your example of problem solving through a step-by step process.

Experts (teachers) in a particular field are often so fluent in solving problems from that field that they can find it difficult to articulate the problem solving principles and strategies they use to novices (students) in their field because these principles and strategies are second nature to the expert. To teach students problem solving skills, a teacher should be aware of principles and strategies of good problem solving in his or her discipline .

The mathematician George Polya captured the problem solving principles and strategies he used in his discipline in the book How to Solve It: A New Aspect of Mathematical Method (Princeton University Press, 1957). The book includes a summary of Polya’s problem solving heuristic as well as advice on the teaching of problem solving.

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Problem-Based Learning

Problem-based learning  (PBL) is a student-centered approach in which students learn about a subject by working in groups to solve an open-ended problem. This problem is what drives the motivation and the learning. 

Why Use Problem-Based Learning?

Nilson (2010) lists the following learning outcomes that are associated with PBL. A well-designed PBL project provides students with the opportunity to develop skills related to:

Working in teams.
Managing projects and holding leadership roles.
Oral and written communication.
Self-awareness and evaluation of group processes.
Working independently.
Critical thinking and analysis.
Explaining concepts.
Self-directed learning.
Applying course content to real-world examples.
Researching and information literacy.
Problem solving across disciplines.

Considerations for Using Problem-Based Learning

Rather than teaching relevant material and subsequently having students apply the knowledge to solve problems, the problem is presented first. PBL assignments can be short, or they can be more involved and take a whole semester. PBL is often group-oriented, so it is beneficial to set aside classroom time to prepare students to  work in groups  and to allow them to engage in their PBL project.

Students generally must:

Examine and define the problem.
Explore what they already know about underlying issues related to it.
Determine what they need to learn and where they can acquire the information and tools necessary to solve the problem.
Evaluate possible ways to solve the problem.
Solve the problem.
Report on their findings.

Getting Started with Problem-Based Learning

Articulate the learning outcomes of the project. What do you want students to know or be able to do as a result of participating in the assignment?
Create the problem. Ideally, this will be a real-world situation that resembles something students may encounter in their future careers or lives. Cases are often the basis of PBL activities. Previously developed PBL activities can be found online through the University of Delaware’s PBL Clearinghouse of Activities .
Establish ground rules at the beginning to prepare students to work effectively in groups.
Introduce students to group processes and do some warm up exercises to allow them to practice assessing both their own work and that of their peers.
Consider having students take on different roles or divide up the work up amongst themselves. Alternatively, the project might require students to assume various perspectives, such as those of government officials, local business owners, etc.
Establish how you will evaluate and assess the assignment. Consider making the self and peer assessments a part of the assignment grade.

Nilson, L. B. (2010). Teaching at its best: A research-based resource for college instructors (2nd ed.).  San Francisco, CA: Jossey-Bass. 

Don’t Just Tell Students to Solve Problems. Teach Them How.

The positive impact of an innovative uc san diego problem-solving educational curriculum continues to grow.

Daniel Kane - [email protected]

Published Date

Not getting stuck

One of the overarching goals of this project is to teach both problem-identification and problem-solving skills that help students avoid getting stuck during the learning process. Stuck feelings lead to frustration – and when it’s a Science, Technology, Engineering and Math (STEM) project, that frustration can lead students to feel they don’t belong in a STEM major or a STEM career. Instead, the UC San Diego curriculum is designed to give students the tools that lead to reactions like “this class is hard, but I know I can do this!” – as Ogo, a celebrated high school biomedical sciences and technology teacher, put it.

Three years into the curriculum development effort, the light-hearted energy of the students combined with their intense focus points to success. On the last day of the class, Mourad Mjahed PhD, Director of the MESA Program at Southwestern College’s School of Mathematics, Science and Engineering came to UC San Diego to see the final project presentations made by his 22 MESA students.

“Industry is looking for students who have learned from their failures and who have worked outside of their comfort zones,” said Mjahed. The UC San Diego problem-solving curriculum, Mjahed noted, is an opportunity for students to build the skills and the confidence to learn from their failures and to work outside their comfort zone. “And from there, they see pathways to real careers,” he said.

What does it mean to explicitly teach problem solving?

This approach to teaching problem solving includes a significant focus on learning to identify the problem that actually needs to be solved, in order to avoid solving the wrong problem. The curriculum is organized so that each day is a complete experience. It begins with the teacher introducing the problem-identification or problem-solving strategy of the day. The teacher then presents case studies of that particular strategy in action. Next, the students get introduced to the day’s challenge project. Working in teams, the students compete to win the challenge while integrating the day’s technique. Finally, the class reconvenes to reflect. They discuss what worked and didn't work with their designs as well as how they could have used the day’s problem-identification or problem-solving technique more effectively.

The challenges are designed to be engaging – and over three years, they have been refined to be even more engaging. But the student engagement is about much more than being entertained. Many of the students recognize early on that the problem-identification and problem-solving skills they are learning can be applied not just in the classroom, but in other classes and in life in general.

Gabriel from Southwestern College is one of the students who saw benefits outside the classroom almost immediately. In addition to taking the UC San Diego problem-solving course, Gabriel was concurrently enrolled in an online computer science programming class. He said he immediately started applying the UC San Diego problem-identification and troubleshooting strategies to his coding assignments.

Gabriel noted that he was given a coding-specific troubleshooting strategy in the computer science course, but the more general problem-identification strategies from the UC San Diego class had been extremely helpful. It’s critical to “find the right problem so you can get the right solution. The strategies here,” he said, “they work everywhere.”

Phan echoed this sentiment. “We believe this curriculum can prepare students for the technical workforce. It can prepare students to be impactful for any career path.”

The goal is to be able to offer the course in community colleges for course credit that transfers to the UC, and to possibly offer a version of the course to incoming students at UC San Diego.

As the team continues to work towards integrating the curriculum in both standardized high school courses such as physics, and incorporating the content as a part of the general education curriculum at UC San Diego, the project is expected to impact thousands more students across San Diego annually.

Portrait of the Problem-Solving Curriculum

On a sunny Wednesday in July 2023, an experiential-learning classroom was full of San Diego community college students. They were about half-way through the three-week problem-solving course at UC San Diego, held in the campus’ EnVision Arts and Engineering Maker Studio. On this day, the students were challenged to build a contraption that would propel at least six ping pong balls along a kite string spanning the laboratory. The only propulsive force they could rely on was the air shooting out of a party balloon.

A team of three students from Southwestern College – Valeria, Melissa and Alondra – took an early lead in the classroom competition. They were the first to use a plastic bag instead of disposable cups to hold the ping pong balls. Using a bag, their design got more than half-way to the finish line – better than any other team at the time – but there was more work to do.

As the trio considered what design changes to make next, they returned to the problem-solving theme of the day: unintended consequences. Earlier in the day, all the students had been challenged to consider unintended consequences and ask questions like: When you design to reduce friction, what happens? Do new problems emerge? Did other things improve that you hadn’t anticipated?

Other groups soon followed Valeria, Melissa and Alondra’s lead and began iterating on their own plastic-bag solutions to the day’s challenge. New unintended consequences popped up everywhere. Switching from cups to a bag, for example, reduced friction but sometimes increased wind drag.

Over the course of several iterations, Valeria, Melissa and Alondra made their bag smaller, blew their balloon up bigger, and switched to a different kind of tape to get a better connection with the plastic straw that slid along the kite string, carrying the ping pong balls.

One of the groups on the other side of the room watched the emergence of the plastic-bag solution with great interest.

“We tried everything, then we saw a team using a bag,” said Alexander, a student from City College. His team adopted the plastic-bag strategy as well, and iterated on it like everyone else. They also chose to blow up their balloon with a hand pump after the balloon was already attached to the bag filled with ping pong balls – which was unique.

“I don’t want to be trying to put the balloon in place when it's about to explode,” Alexander explained.

Asked about whether the structured problem solving approaches were useful, Alexander’s teammate Brianna, who is a Southwestern College student, talked about how the problem-solving tools have helped her get over mental blocks. “Sometimes we make the most ridiculous things work,” she said. “It’s a pretty fun class for sure.”

Yoshadara, a City College student who is the third member of this team, described some of the problem solving techniques this way: “It’s about letting yourself be a little absurd.”

Alexander jumped back into the conversation. “The value is in the abstraction. As students, we learn to look at the problem solving that worked and then abstract out the problem solving strategy that can then be applied to other challenges. That’s what mathematicians do all the time,” he said, adding that he is already thinking about how he can apply the process of looking at unintended consequences to improve both how he plays chess and how he goes about solving math problems.

Looking ahead, the goal is to empower as many students as possible in the San Diego area and beyond to learn to problem solve more enjoyably. It’s a concrete way to give students tools that could encourage them to thrive in the growing number of technical careers that require sharp problem-solving skills, whether or not they require a four-year degree.

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Teaching problem solving

Strategies for teaching problem solving apply across disciplines and instructional contexts. First, introduce the problem and explain how people in your discipline generally make sense of the given information. Then, explain how to apply these approaches to solve the problem.

Introducing the problem

Explaining how people in your discipline understand and interpret these types of problems can help students develop the skills they need to understand the problem (and find a solution). After introducing how you would go about solving a problem, you could then ask students to:

frame the problem in their own words
define key terms and concepts
determine statements that accurately represent the givens of a problem
identify analogous problems
determine what information is needed to solve the problem

Working on solutions

In the solution phase, one develops and then implements a coherent plan for solving the problem. As you help students with this phase, you might ask them to:

identify the general model or procedure they have in mind for solving the problem
set sub-goals for solving the problem
identify necessary operations and steps
draw conclusions
carry out necessary operations

You can help students tackle a problem effectively by asking them to:

systematically explain each step and its rationale
explain how they would approach solving the problem
help you solve the problem by posing questions at key points in the process
work together in small groups (3 to 5 students) to solve the problem and then have the solution presented to the rest of the class (either by you or by a student in the group)

In all cases, the more you get the students to articulate their own understandings of the problem and potential solutions, the more you can help them develop their expertise in approaching problems in your discipline.

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Evan Glazer (University of Georgia)

Editor’s Note: Dr. Glazer chose to use the term Problem-based Instruction and Inquiry, but my reading and other references to this chapter also use the term Problem-based Learning. The reader can assume the terms are equivalent.

Description

Problem-based inquiry is an effort to challenge students to address real-world problems and resolve realistic dilemmas.

Such problems create opportunities for meaningful activities that engage students in problem solving and higher-ordered thinking in authentic settings. Many textbooks attempt to promote these skills through contrived settings without relevance to students’ lives or interests. A notorious algebra problem concerns the time at which two railway trains will pass each other:

Two trains leave different stations headed toward each other. Station A is 500 miles west of Station B. Train A leaves station A at 12:00 pm traveling toward Station B at a rate of 60 miles per hour. Train B leaves Station B at 2:30 pm for Station A at a rate of 45 miles per hour. At what time will the trains meet?

Reading this question, one might respond, “Who cares?”, or, “Why do we need to know this?” Such questions have created substantial anxiety among students and have, perhaps, even been the cause of nightmares. Critics would argue that classic “story problems” leave a lasting impression of meaningless efforts to confuse and torment students, as if they have come from hell’s library. Problem-based inquiry, on the other hand, intends to engage students in relevant, realistic problems.

Several changes would need to be made in the above problem to promote problem-based inquiry. It would first have to be acknowledged that the trains are not, in fact, traveling at constant rates when they are in motion; negotiating curves or changing tracks at high speeds can result in accidents.

Further, all of the information about the problem cannot be presented to the learner at the outset; that is, some ambiguity must exist in the context so that students have an opportunity to engage in a problem-solving activity. In addition, the situation should involve a meaningful scenario. Suppose that a person intends to catch a connecting train at the second station and requires a time-efficient itinerary? What if we are not given data about the trains, but instead, the outcome of a particular event, such as an accident?

Why should we use problem-based inquiry to help students learn?

The American educational system has been criticized for having an underachieving curriculum that leads students to memorize and regurgitate facts that do not apply to their lives (Martin, 1987; Paul, 1993). Many claim that the traditional classroom environment, with its orderly conduct and didactic teaching methods in which the teacher dispenses information, has greatly inhibited students’ opportunities to think critically (Dossey et al., 1988; Goodlad, 1984; Wood, 1987). Problem-based inquiry is an attempt to overcome these obstacles and confront the concerns presented by the National Assessment of Educational Progress:

If an unfriendly foreign power had attempted to impose on America the mediocre educational performance that exists today, we might well have viewed it as an act of war. We have, in effect, been committing an act of unthinking, unilateral educational disarmament. (A Nation at Risk, 1983)

Problem-based inquiry emphasizes learning as a process that involves problem solving and critical thinking in situated contexts. It provides opportunities to address broader learning goals that focus on preparing students for active and responsible citizenship. Students gain experience in tackling realistic problems, and emphasis is placed on using communication, cooperation, and resources to formulate ideas and develop reasoning skills.

What is a framework for a problem-based inquiry?

Situated cognition, constructivism, social learning, and communities of practice are assumed theories of learning and cognition in problem-based inquiry environments. These theories have common themes about the context and the process of learning and are often associated.

Characteristics

Some common characteristics in problem-based learning models:

Activity is grounded in a general question about a problem that has multiple possible answers and methods for addressing the question. Each problem has a general question that guides the overall task followed by ill-structured problems or questions that are generated throughout the problem-solving process. That is, to address the larger question, students must derive and investigate smaller problems or questions that relate to the findings and implications of the broader goal. The problems or questions thus created are most likely new to the students and lack known definitive methods or answers that have been predetermined by the teacher.

Learning is student-centered; the teacher acts as facilitator. In essence, the teacher creates an environment where students take ownership in the direction and content of their learning.

Students work collaboratively towards addressing the general question . All of the students work together to attain the shared goal of producing a solution to the problem. Consequently, the groups co-depend on each other’s performance and contributions in order to make their own advances in reasoning toward answering the research questions and the overall problem.

Learning is driven by the context of the problem and is not bound by an established curriculum. In this environment, students determine what and how much they need to learn in order to accomplish a specific task. Consequently, acquired information and learned concepts and strategies are tied directly to the context of the learning situation. Learning is not confined to a preset curriculum. Creation of a final product is not a necessary requirement of all problem-based inquiry models.

Project-based learning models most often include this type of product as an integral part of the learning process, because learning is expected to occur primarily in the act of creating something. Unlike problem based inquiry models, project-based learning does not necessarily address a real-world problem, nor does it focus on providing argumentation for resolution of an issue.

In a problem-based inquiry setting, there is greater emphasis on problem-solving, analysis, resolution, and explanation of an authentic dilemma. Sometimes this analysis and explanation is represented in the form of a project, but it can also take the form of verbal debate and written summary.

Instructional models and applications

There is no single method for designing problem-based inquiry learning environments.

Various techniques have been used to generate the problem and stimulate learning. Promoting student-ownership, using a particular medium to focus attention, telling stories, simulating and recreating events, and utilizing resources and data on the Internet are among them. The instructional model, problem based learning will be discussed next with attention to instructional strategies and practical examples.

Problem-Based Learning

Problem-based learning (PBL) is an instructional strategy in which students actively resolve complex problems in realistic situations.

It can be used to teach individual lessons, units, or even entire curricula. PBL is often approached in a team environment with emphasis on building skills related to consensual decision making, dialogue and discussion, team maintenance, conflict management, and team leadership. While the fundamental approach of problem solving in situated environments has been used throughout the history of schooling, the term PBL did not appear until the 1970s and was devised as an alternative approach to medical education.

In most medical programs, students initially take a series of fact intensive courses in biology and anatomy and then participate in a field experience as a medical resident in a hospital or clinic. However, Barrows reported that, unfortunately, medical residents frequently had difficulty applying knowledge from their classroom experiences in work-related, problem-solving situations. He argued that the classical framework of learning medical knowledge first in classrooms through studying and testing was too passive and removed from context to take on meaning.

Consequently, PBL was first seen as a medical field immersion experience whereby students learned about their medical specialty through direct engagement in realistic problems and gradual apprenticeship in natural or simulated settings. Problem solving is emphasized as an initial area of learning and development in PBL medical programs more so than memorizing a series of facts outside their natural context.

In addition to the field of medicine, PBL is used in many areas of education and training. In academic courses, PBL is used as a tool to help students understand the utility of a particular concept or study. For example, students may learn about recycling and materials as they determine methods that will reduce the county landfill problem.

In addition, alternative education programs have been created with a PBL emphasis to help at-risk students learn in a different way through partnerships with local businesses and government. In vocational education, PBL experiences often emphasize participation in natural settings.

For example, students in architecture address the problem of designing homes for impoverished areas. Many of the residents need safe housing and cannot afford to purchase typical homes. Consequently, students learn about architectural design and resolving the problem as they construct homes made from recycled materials. In business and the military, simulations are used as a means of instruction in PBL. The affective and physiological stress associated with warfare can influence strategic planning, so PBL in military settings promotes the use of “war games” as a tactic for facing authentic crises.

In business settings, simulations of “what if” scenarios are used to train managers in various strategies and problem-solving approaches to conflict resolution. In both military and business settings, the simulation is a tool that provides an opportunity to not only address realistic problems but to learn from mistakes in a more forgiving way than in an authentic context.

Designing the learning environment

The following elements are commonly associated with PBL activities.

Problem generation: The problems must address concepts and principles relevant to the content domain. Problems are not investigated by students solely for problem solving experiences but as a means of understanding the subject area. Some PBL activities incorporate multidisciplinary approaches, assuming the teacher can provide and coordinate needed resources such as additional content, instructional support, and other teachers. In addition, the problems must relate to real issues that are present in society or students’ lives. Contrived scenarios detract from the perceived usefulness of a concept.

Problem presentation: Students must “own” the problem, either by creating or selecting it. Ownership also implies that their contributions affect the outcome of solving the problem. Thus, more than one solution and more than one method of achieving a solution to the problem are often possible. Furthermore, ownership means that students take responsibility for representing and communicating their work in a unique way.

Predetermined formats of problem structure and analysis towards resolution are not recommended; however, the problem should be presented such that the information in the problem does not call attention to critical factors in the case that will lead to immediate resolution. Ownership also suggests that students will ask further questions, reveal further information, and synthesize critical factors throughout the problem-solving process.

Teacher role: Teachers act primarily as cognitive coaches by facilitating learning and modeling higher order thinking and meta cognitive skills. As facilitators, teachers give students control over how they learn and provide support and structure in the direction of their learning. They help the class create a common framework of expectations using tools such as general guidelines and time lines.

As cognitive modelers, teachers think aloud about strategies and questions that influence how students manage the progress of their learning and accomplish group tasks. In addition, teachers continually question students about the concepts they are learning in the context of the problem in order to probe their understanding, challenge their thinking, and help them deepen or extend their ideas.

Student role: Students first define or select an ill-structured problem that has no obvious solution. They develop alternative hypotheses to resolve the problem and discuss and negotiate their conjectures in a group. Next, they access, evaluate, and utilize data from a variety of available sources to support or refute their hypotheses. They may alter, develop, or synthesize hypotheses in light of new information. Finally, they develop clearly stated solutions that fit the problem and its inherent conditions, based upon information and reasoning to support their arguments. Solutions can be in the form of essays, presentations, or projects.

Maine School Engages Kids With Problem-Solving Challenges (11:37)

https://youtu.be/i17F-b5GG94

[PBS NewsHour].(2013, May 6). Maine School Engages Kids with Problem Solving Challenges. [Video File]. Retrieve from https://youtu.be/i17F-b5GG94

Special correspondent John Tulenko of Leaning Matters reports on a public middle school in Portland, Maine that is taking a different approach to teaching students. Teachers have swapped traditional curriculum for an unusually comprehensive science curriculum that emphasizes problem-solving, with a little help from some robots.

Effectiveness of Problem and Inquiry-based learning.

Why does inquiry-based learning only have an effect size of 0.31 when it is an approach to learning that seems to engage students and teachers so readily in the process of learning?

When is the right and wrong time to introduce inquiry and problem based learning?

Watch video from John Hattie on inquiry and problem-based learning, (2:11 minutes).

[Corwin]. (2015, Nov. 9). John Hattie on inquiry-based learning. [Video File]. Retrieved from https://youtu.be/YUooOYbgSUg.

Glazer, E. (2010) Emerging Perspectives on Learning, Teaching, and Technology, Global Text, Michael Orey. (Chapter 14) Attribution CC 3.0. Retrieved from https://textbookequity.org/Textbooks/Orey_Emerging_Perspectives_Learning.pdf

Instructional Methods, Strategies and Technologies to Meet the Needs of All Learners Copyright © 2017 by Evan Glazer (University of Georgia) is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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New Designs for School 5 Steps to Teaching Students a Problem-Solving Routine

Jeff Heyck-Williams (He, His, Him) Director of the Two Rivers Learning Institute in Washington, DC

We’ve all had the experience of truly purposeful, authentic learning and know how valuable it is. Educators are taking the best of what we know about learning, student support, effective instruction, and interpersonal skill-building to completely reimagine schools so that students experience that kind of purposeful learning all day, every day.

Students can use the 5 steps in this simple routine to solve problems across the curriculum and throughout their lives.

When I visited a fifth-grade class recently, the students were tackling the following problem:

If there are nine people in a room and every person shakes hands exactly once with each of the other people, how many handshakes will there be? How can you prove your answer is correct using a model or numerical explanation?

There were students on the rug modeling people with Unifix cubes. There were kids at one table vigorously shaking each other’s hand. There were kids at another table writing out a diagram with numbers. At yet another table, students were working on creating a numeric expression. What was common across this class was that all of the students were productively grappling around the problem.

On a different day, I was out at recess with a group of kindergarteners who got into an argument over a vigorous game of tag. Several kids were arguing about who should be “it.” Many of them insisted that they hadn’t been tagged. They all agreed that they had a problem. With the assistance of the teacher they walked through a process of identifying what they knew about the problem and how best to solve it. They grappled with this very real problem to come to a solution that all could agree upon.

Then just last week, I had the pleasure of watching a culminating showcase of learning for our 8th graders. They presented to their families about their project exploring the role that genetics plays in our society. Tackling the problem of how we should or should not regulate gene research and editing in the human population, students explored both the history and scientific concerns about genetics and the ethics of gene editing. Each student developed arguments about how we as a country should proceed in the burgeoning field of human genetics which they took to Capitol Hill to share with legislators. Through the process students read complex text to build their knowledge, identified the underlying issues and questions, and developed unique solutions to this very real problem.

Problem-solving is at the heart of each of these scenarios, and an essential set of skills our students need to develop. They need the abilities to think critically and solve challenging problems without a roadmap to solutions. At Two Rivers Public Charter School in Washington, D.C., we have found that one of the most powerful ways to build these skills in students is through the use of a common set of steps for problem-solving. These steps, when used regularly, become a flexible cognitive routine for students to apply to problems across the curriculum and their lives.

The Problem-Solving Routine

At Two Rivers, we use a fairly simple routine for problem solving that has five basic steps. The power of this structure is that it becomes a routine that students are able to use regularly across multiple contexts. The first three steps are implemented before problem-solving. Students use one step during problem-solving. Finally, they finish with a reflective step after problem-solving.

Problem Solving from Two Rivers Public Charter School

Before Problem-Solving: The KWI

The three steps before problem solving: we call them the K-W-I.

The “K” stands for “know” and requires students to identify what they already know about a problem. The goal in this step of the routine is two-fold. First, the student needs to analyze the problem and identify what is happening within the context of the problem. For example, in the math problem above students identify that they know there are nine people and each person must shake hands with each other person. Second, the student needs to activate their background knowledge about that context or other similar problems. In the case of the handshake problem, students may recognize that this seems like a situation in which they will need to add or multiply.

The “W” stands for “what” a student needs to find out to solve the problem. At this point in the routine the student always must identify the core question that is being asked in a problem or task. However, it may also include other questions that help a student access and understand a problem more deeply. For example, in addition to identifying that they need to determine how many handshakes in the math problem, students may also identify that they need to determine how many handshakes each individual person has or how to organize their work to make sure that they count the handshakes correctly.

The “I” stands for “ideas” and refers to ideas that a student brings to the table to solve a problem effectively. In this portion of the routine, students list the strategies that they will use to solve a problem. In the example from the math class, this step involved all of the different ways that students tackled the problem from Unifix cubes to creating mathematical expressions.

This KWI routine before problem solving sets students up to actively engage in solving problems by ensuring they understand the problem and have some ideas about where to start in solving the problem. Two remaining steps are equally important during and after problem solving.

The power of teaching students to use this routine is that they develop a habit of mind to analyze and tackle problems wherever they find them.

During Problem-Solving: The Metacognitive Moment

The step that occurs during problem solving is a metacognitive moment. We ask students to deliberately pause in their problem-solving and answer the following questions: “Is the path I’m on to solve the problem working?” and “What might I do to either stay on a productive path or readjust my approach to get on a productive path?” At this point in the process, students may hear from other students that have had a breakthrough or they may go back to their KWI to determine if they need to reconsider what they know about the problem. By naming explicitly to students that part of problem-solving is monitoring our thinking and process, we help them become more thoughtful problem solvers.

After Problem-Solving: Evaluating Solutions

As a final step, after students solve the problem, they evaluate both their solutions and the process that they used to arrive at those solutions. They look back to determine if their solution accurately solved the problem, and when time permits they also consider if their path to a solution was efficient and how it compares to other students’ solutions.

The power of teaching students to use this routine is that they develop a habit of mind to analyze and tackle problems wherever they find them. This empowers students to be the problem solvers that we know they can become.

Jeff Heyck-Williams (He, His, Him)

Director of the two rivers learning institute.

Jeff Heyck-Williams is the director of the Two Rivers Learning Institute and a founder of Two Rivers Public Charter School. He has led work around creating school-wide cultures of mathematics, developing assessments of critical thinking and problem-solving, and supporting project-based learning.

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Problem-Based Learning

1 Understand
2 Get Started
3 Train Your Peers
4 Related Links

What is Problem-Based Learning

Problem-based learning & the classroom, the problem-based learning process, problem-based learning & the common core, project example: a better community, project example: preserving appalachia, project example: make an impact.

All Toolkits

A Learning is Open toolkit written by the New Learning Institute.

Problem-based learning (PBL) challenges students to identify and examine real problems, then work together to address and solve those problems through advocacy and by mobilizing resources. Importantly, every aspect of the problem solving process involves students in real work—work that is a reflection of the range of expertise required to solve issues in the world outside of school.

While problem-based learning can use any type of problem as its basis, the approach described here is deliberately focused on local ones. Local problems allow students to have a meaningful voice, and be instrumental in a process where real, recognizable change results. It also gives students opportunities to source and interact with a variety of local experts.

In many classrooms teachers give students information and then ask them to solve problems at the culmination of a unit. Problem-based learning turns this on its head by challenging students to define the problem before finding the resources necessary to address or solve it. In this approach, teachers are facilitators: they set the context for the problem, ask questions to propel students’ interests and learning forward, help students locate necessary resources and experts, and provide multiple opportunities to critique students’ process and progress. In some cases, the teacher may identify a problem that is connected to existing curriculum; in others the teacher may assign a larger topic and challenge the students to identify a specific problem they are interested in addressing.

This approach is interdisciplinary and provides natural opportunities for integrating a variety of required content areas. Because recognizing and making relationships between content areas is a necessary part of the problem-solving process—as it is in the real world—students are building skills to prepare them for life, work, and civic participation. Problem-based learning gives students a variety of ways to address and tackle a problem. It encourages everyone to contribute and rewards different kinds of success. This builds confidence in students who have not always been successful in school. With the changing needs of today’s world, there is a growing urgency for people who are competent in a range of areas including the ability to apply critical thinking to complex problems, collaborate, network and gather resources, and communicate and persuade others to actively take up a cause.

Problem-based learning builds agency & independence

Although students work collaboratively throughout the process, applying a wide range of skills to new tasks requires them to develop their own specialties that lead to greater confidence and competency. And because the process is student-driven, students are challenged to define the problem, conduct comprehensive research, sort through multiple solutions and present the one that allows them best move forward. This reinforces a sense of self-agency and independence.

Problem-based learning promotes adaptability & flexibility

Investigating and solving problems requires students to work with many different types of people and encounter many unknowns throughout the process. These experiences help students learn to be adaptable and flexible during periods of uncertainty. From an academic standpoint, this flexible mindset is an opportunity for students to develop a range of communication aptitudes and styles. For example, in the beginning research phases, students must gather multiple perspectives and gain a clear understanding of their various audiences. As they move into the later project phases they must develop more nuanced ways to communicate with each audience, from clearly presenting information to persuasion to defending the merits of a new idea.

Problem-based learning is persistent

Educators recognize that when students are working towards a real goal they care about, they show increased investment and willingness to persist through challenges. Problem-based learning requires students to navigate many variables including the diverse personalities on a project team, the decisions and perspectives of stakeholders, challenging and rigorous content, and real world deadlines. Students will experience frustration and failure, but they will learn that working though that by trying new things will be its own reward. And this is a critical lesson that will be carried on into life and work.

Problem-based learning is civically engaged

Because problem-based learning focuses on using local issues as jumping off points it gives students a meaningful context in which to voice their opinions and take the initiative to find solutions. Problems within schools and communities also provide opportunities for students to work directly with stakeholders (i.e. the school principal or a town council member) and experts (i.e. local residents, professionals, and business owners). These local connections make it more likely that students will successfully implement some aspect of their plan and gives students firsthand experience with civic processes.

A problem well put is half solved. – John Dewey

The problem-based learning process described in this toolkit has been refined and tested through the Model Classroom Program, a project of the New Learning Institute. Educators throughout the United States participated in this program by designing, implementing, and documenting projects. The resulting problem-based learning approach provides a clear process and diverse set of tools to support problem-based learning.

The problem-based learning process can help students define problems in new ways, explore multiple perspectives, challenge their thinking, and develop the real-world skills needed for planning and carrying out a project. Beyond this, because the approach emphasizes local and community-based issues, this process develops student drive and motivation as they work towards a tangible end result with the potential to impact their community.

Make it Real

The world is full of unsolved problems and opportunities just waiting to be addressed. The Make It Real phase is about identifying a real problem within the local community, then conducting further investigation to define the problem.

Identify what you do and don’t know about the problem Brainstorm what is known about the problem. What do you know about it at the local level? Is this problem globally relevant? How? What questions would you investigate further?

Discover the problem’s root causes and impacts on the community While it’s easy to find a problem, it’s much harder to understand it. Investigate how the problem impacts different people and places. As a result of these investigations, students will gain a clearer understanding of the problem.

Make it Relevant

Problems are everywhere, but it can often be difficult to convince people that a specific problem should matter to them. The word relevant is from the Latin root meaning “to raise” or “to lift up.” To Make It Relevant, elevate the problem so that people in the community and beyond will take interest and become invested in its resolution. Make important connections in order to begin a plan to address the problem.

Field Studies

Collect as much information as possible on the problem. Conduct the kind of research experts in the field—scientists and historians—conduct. While online and library research is a good starting point, it’s important that students get out into the real world to conduct their own original research! This includes using methods such as surveys, interviews, photo and video documentation, collection of evidence (such as science related activities), and working with a variety of experts and viewpoints.

Develop an action-plan Have students analyze their field studies data and create charts, graphs, and other visual representations to understand their findings. After analyzing, students will have the information needed to develop a plan of action. Importantly, they’ll need to consider how best to meet the needs of all stakeholders, which will include a diverse community such as local businesses, community members, experts, and even the natural world.

Make an Impact

Make An Impact with a creative implementation based on the best research-supported ideas. In many cases, making an impact is about solving the problem. Sometimes it’s about addressing it, making representations to stakeholders, or presenting a possible solution for future implementation. At the most rigorous level, students will implement a project that has lasting impact on their community.

Put your plan into action See the hard work of researching and analyzing the problem pay off as students begin implementing their plans. In so doing, they’ll act as part of a team creating a product to share. Depending on the problem, purpose, and audience, their products might be anything from a website to an art installation to the planning of a community-wide event.

Share your findings and make an impact Share results with important stakeholders and the larger community. Depending on the project, this effort may include awareness campaigns, a persuasive presentation to stakeholders, an action-oriented campaign, a community-wide event, or a re-designed program. In many cases this “final” act leads to the beginning of another project!

With the Common Core implementation, teachers have found different strategies and resources to help align their practice to the standards. Indeed, many schools and districts have discovered a variety of solutions. When considering Common Core alignment, the opportunity presented by methods like problem-based learning hinges on a belief in the art of teaching and the importance of developing students’ passion and love of learning. In short, with the ultimate goal of making students college-, career-, and life-ready, it’s essential that educators put students in the driver’s seat to collaboratively solve real problems.

The Common Core ELA standards draw a portrait of a college- and career-ready student. This portrait includes characteristics such as independence, the ability to adapt communication to different audiences and purposes, the ability to comprehend and critique, appreciation for the value of evidence (when reading and when creating their own work), and the capability to make strategic use of digital media. Developing creative solutions to complex problems provides students with multiple opportunities to develop all of these skills.

Independence

Students are challenged to define the problem and conduct comprehensive research, then present solutions. This student-driven process requires students to find multiple answers and think critically about the best way to act, ultimately building confidence and independence.

Adapting Communication to Different Audiences and Purposes

In the initial research phases, students must gather multiple perspectives and gain a clear understanding of who those audiences are. As they move into the later project phases, they must communicate in a variety of ways (including informative and persuasive methods) to reach diverse audiences.

Comprehending and Critiquing

In examining multiple perspectives, students must summarize various viewpoints, addressing their strengths and critiquing their weaknesses. Furthermore, as students develop solutions they must analyze each idea for its potential success, which compels them to critique their own work in addition to the work of others.

Valuing Evidence

Collecting evidence is essential to the process, whether through visual documentation of a problem, uncovering key facts, or collecting narratives from the community.

Strategic Use of Digital Media

The use of digital media is naturally integrated throughout the entire process. The problem-based learning approach not only builds the specific 21st century skills called for by the Common Core, it also embraces practices supported by hundreds of years of educational theory. This is not the next new thing – problem-based learning is one example of how vetted best educational practices will meet the needs of a future economy and society; and, more immediately, the new Common Core Standards.

Language Arts

The Key Design Considerations for the English Language Arts standards describe an integrated literacy model in which all communication processes are closely connected. Likewise, the problem-based learning approach expects students to read, write, and speak about the issue (whether through interviews or speeches) in a variety of ways (expository, persuasive). In addition, the Key Design Considerations describe how literacy is a shared responsibility across subject areas. Because problem-based learning is rooted in real issues, these naturally connect to science content areas (environmental sciences, engineering and design, innovation and invention), social studies (community history, geography/land forms), math (including operations such as graphing, statistics, economics, and mathematical modeling), and art. As part of this interdisciplinary model, problem-based learning follows a process that touches on key ELA skill areas including research, a variety of writing styles and formats (both reading and writing in these formats), publishing, and integration of digital media.

It’s also important to note that the Common Core calls for an increase in informational and nonfiction text. This objective is easily met through examining real problems. Quite simply, informational and nonfiction text is everywhere – in newspaper articles, public surveys, government documents, etc. Very often, when reading out of context, many students struggle to work through and comprehend these types of complex texts. Because problem-based learning authentically integrates a real purpose with reading informational text, students work harder to comprehend and apply their reading.

Note: Each project has the potential to meet many additional standards. The standards outlined here are only a sampling of those addressed by this approach.

Reading Standards

CCSS.ELA-Literacy.CCRA.R.6 Assess how point of view or purpose shapes the content and style of a text. In the early phases of problem-based learning, students investigate the topic by reading a range of informational and persuasive texts, and by talking to a variety of experts and community members. As an essential component to these investigations on multiple perspectives, students must be able to understand the purpose of the text, the intended audience, and the individual’s position on the issue (if applicable).

CCSS.ELA-Literacy.CCRA.R.7 Integrate and evaluate content presented in diverse media and formats, including visually and quantitatively, as well as in words. As students consider multiple perspectives on their identified problem, they naturally will seek a wide range of print materials, media resources (videos, presentations), and formats (research studies, opinion pieces). Comparing and contrasting the viewpoints of these various texts will help students shape a more holistic view of the problem.

Writing Standards

CCSS.ELA-Literacy.CCRA.W.1 Write arguments to support claims in an analysis of substantive topics or texts using valid reasoning and relevant and sufficient evidence. As students analyze the problem, multiple opportunities for persuasive writing emerge. In the early project phases, students might summarize their perspective on the problem using key evidence from a variety of research (online, community polling, and discussions with experts). In the later project phases, students might develop a proposal or presentation to persuade others to change personal habits or consider a larger change in the community.

Speaking & Listening Standards

CCSS.ELA-Literacy.CCRA.SL.1 Prepare for and participate effectively in a range of conversations and collaborations with diverse partners, building on others’ ideas and expressing their own clearly and persuasively. Multiple perspectives are an essential component to any problem-based project. As students investigate, they must seek a wide range of opinions and personal stories on the issues. Furthermore, this process is collaborative. Students must trust and work with each other, they must trust and work with key experts, and, in some cases, they must convince others to collaborate with them around a shared purpose or cause.

CCSS.ELA-Literacy.CCRA.SL.5 Make strategic use of digital media and visual displays of data to express information and enhance understanding of presentations. Because each problem-based project requires students to analyze information, share their findings with others, and collaborate on a variety of levels, digital media is naturally integrated into these tasks. Students might create charts, graphs, or other illustrative/photo/video displays to communicate their research results. Students might use a variety of digital formats including graphic posters, video public service announcements (PSAs), and digital presentations to mobilize the community to their cause. Inherent to these processes is special consideration of how images, videos, and other media support key ideas and key evidence and further the effectiveness of their presentation on the intended audience.

Mathematics

Simply put, math is problem solving. Problem-based learning provides multiple opportunities for students to apply and develop their understanding of various mathematical concepts within real contexts. Through the various stages of problem-based learning, students engage in the same dispositions encouraged by the Standards for Mathematical Practice

CCSS.Math.Practice.MP1 Make sense of problems and persevere in solving them. Problem-based learning is all about problem solving. An essential first step is understanding the problem as deeply as possible, rather than rushing to solve it. This is a process that takes time and perseverance, both individually and in collaborative student groups.

CCSS.Math.Practice.MP3 Construct viable arguments and critique the reasoning of others. As students understand and deconstruct a problem, they must begin to form solutions. As part of this process, they must have evidence (including visual and mathematical evidence) to support their position. They must also understand other perspectives to solving the problem, and they must be prepared to critique those other perspectives based on verbal and mathematical reasoning.

CCSS.Math.Practice.MP4 Model with mathematics. Throughout the process, students must analyze information and data using a variety of mathematical models. These range from charts and graphs to 3-D modeling used in science or engineering projects.

CCSS.Math.Practice.MP5 Use appropriate tools strategically. According to the Common Core Math Practices standard, “Mathematically proficient students consider the available tools when solving a mathematical problem. These tools might include pencil and paper, concrete models, a ruler, a protractor, a calculator, a spreadsheet, a computer algebra system, a statistical package, or dynamic geometry software.” In addition to providing opportunities to use these tools, problem-based learning asks students to make effective use of digital and mobile media as they collect information, document the issue, share their findings, and mobilize others to their cause.

Students informed the school about the importance of recycling, developed systems to improve recycling options and implemented a school-wide recycling program that involved all students, other teachers, school principals, school custodians, and the county recycling center.

While investigating their local county history, students were challenged to recognize their role in the community and ultimately realize the importance of stewardship for the county’s land, history and culture. Students began by researching their local history through many first hand experiences including museum visits, local resident interviews and visits to places representing the current culture.

Challenged to find ways to make “A Better Community”, students chose to investigate recycling.

They conducted hands-on research to determine the need for a recycling program through a school survey, town trash pickup and visit to the local Landfill and Recycling Center.

Students then developed a proposal for a school-wide recycling program, interviewed the principal to address their concerns and began to carry out their plan.

Students designed recycling bins for each classroom and worked with school janitors to develop a plan for collection.

Students visited each classroom to distribute the recycling bins and describe how to use them. Students developed a schedule for collecting bins and sorting materials. The program continues beyond the initial school-year; students continue to expand their efforts.

Students created Project Playhouse, a live production for the local community. Audience members included community members, parents, and other students. In addition, students designed a quilt sharing Appalachian history, and recorded their work on a community website.

Appalachia has a rich culture full of unique traditions and an impressive heritage, yet many negative stereotypes persist. 6th grade students brainstormed existing stereotypes and their consequences on the community.

Students discussions led them to realize that, in their region, stereotypes were preventing people from overcoming adversity. They set about to conduct further research demonstrating the strengths of Appalachian heritage.

Students investigated Appalachian culture by working with local experts like Tammy Horn, professor at Eastern Kentucky University and specialist in Appalachian cultural traditions; taking a field trip to Logan Hubble Park to explore the natural region; talking with a “coon” hunter and other local Appalachians including quilters, cooks, artists, and writers.

Students developed a plan to curate an exhibition and live production for the local community. Finally, students connected virtually with museum expert Rebecca Kasemeyer, Associate Director of Education at the Smithsonian National Portrait Gallery to discuss exhibition design.

For their final projects students produced a series of works exhibiting Appalachian life, work, play and community structure including a quilt, a theatrical performance and a website.

Students invited the community to view their exhibit and theatrical performance.

Engineering teacher Bryan Coburn presented a scenario to his students inspired by the community’s very real drought, a drought so bad that cars could only be washed on specific days. Students identified and examined environmental issues related to water scarcity in their community.

Based on initial brainstorming, students divided into teams based on specific problems related to a water shortage. These included topics like watering gardens and lawns, watering cars, drinking water to name a few.

Based on their topic, students conducted online research on existing solutions to their specific problem.

Students analyzed their research to develop their own prototypes and plans for addressing the problem. Throughout the planning phase students received peer and teacher feedback on the viability of their prototypes, resulting in many edits before final designs were selected for creation.

Students created online portfolios showcasing their research, 3D designs, and multimedia presentations marketing their designs. Student portfolios included documentation of each stage of the design process, a design brief, decision matrix, a prototype using Autodesk Inventor 3D professional modeling tool, and a final presentation.

Students shared their presentations and portfolios in a public forum, pitching their proposed solution to a review committee consisting of local engineers from the community, the city water manager and the school principal.

Plan Your PBL Experience

Resources to help you plan.

Problem-based learning projects are inspired by students’ real world experiences and the pressing issues and concerns they want to address. Problem-based learning projects benefit teachers by increasing student motivation and engagement, while deepening knowledge and improving essential skills. In spite of the inherent value problem-based learning brings to any educational setting, planning a large project can be an overwhelming task.

Through the New Learning Institute’s Model Classroom, a range of problem-based learning planning tools have been developed and tested in a variety of educational settings. These tools make the planning process more manageable by supporting teachers in establishing the context and/or problem for a project, planning for and procuring the necessary resources for a real-world project (including community organizations, expert involvement, and tools needed for communicating, creating and sharing), and facilitating students through the project phases.

Here are some initial considerations when planning a problem-based learning project. (More detailed tips and planning tools follow.) These questions can help you determine where to begin your project planning. Once you have a clear idea, the problem-based learning planning tools will guide you through the process.

Are you starting from the curriculum? It’s probably tempting to jump in and define a problem for students based on the unit of study. And time constraints may make a teacher-defined problem necessary. If time permits, a problem-based learning project will be more successful if time is built-in for students to define a problem they’d like to address. Do this by building in topic exploration time, and then challenging students to define a problem based on their findings. Including this extra time will allow students to develop their own interests and questions about the topic, deepening engagement and ensuring that students are investigating a problem they’re invested in—all while covering curriculum requirements.

Are you starting from student interest? Perhaps your students want to solve a problem in the school, such as bullying or lack of recycling. Perhaps they’re concerned about a larger community problem, such as a contested piece of parkland that is up for development or a pollution problem in your local waterways. Starting with student interest can help ensure students’ investment and motivation. However, this starting point provides less direct navigation than existing projects or curriculum materials. When taking on a project of this nature, be sure to identify natural intersections with your curriculum. It also helps to enlist community or expert support.

Start Small – Focus on Practices as Entry Points

If you’re new to problem-based learning it makes sense to start small. Many teachers new to this approach report that starting with the smaller practices—such as integrating research methods or having students define a specific problem within a unit of study—ultimately sets the stage for larger projects and more easily leads them to implement a problem-based learning project.

Opportunities to address and solve problems are everywhere. Just look in your own backyard or schoolyard. Better yet, ask students to identify problems within the school community or based on a topic of interest within a unit of study. As you progress through the project, find natural opportunities for research and problem solving by working with the people who are affected by the issue and invested in solving it. Finally, make sure students share their work with an authentic audience who cares about the problem and its resolution.

Be Honest About Project Constraints

When you’re new to problem-based learning, the most important consideration is your project constraints. For example, perhaps you’re required to cover a designated set of standards and content. Or perhaps you have limited time for this project experience. Whatever the constraints, determine them in advance then plan backwards to determine the length and depth of your project.

Identify Intersections With Your Curriculum

Problem-based learning projects are interdisciplinary and have the ability to meet a range of standards. Identify where these intersections naturally occur with the topic students have selected, then design some activities or project requirements to ensure these content areas are covered.

Turn Limitations Into Opportunities

Many educators work in schools with pre-defined curriculum or schedule constraints that make implementing larger projects difficult. In these cases, it may help to find small windows of opportunity during the school day or after school to implement problem-based learning. For example, some teachers implement problem-based learning in special subject courses which have a more flexible curriculum. Others host afterschool “Genius Hour” programs that challenge students to explore and investigate their interests. Whatever opportunity you find, make the work highly visible to staff and parents. Make it an intention to get the school community exploring and designing possibilities of integrating these practices more holistically.

Take Risks and Model Perseverance

The problem-based learning process is messy and full of opportunities to fail, just like real life and real jobs. Many educators share that this is incredibly difficult for their students and themselves. Despite the initial letdown that comes with small failures, it’s important that students see the value in learning from failure and persevering through these challenges. Model risk taking for your students and when you make a mistake or face a challenge, welcome it with open arms by demonstrating what you’ve learned and what you’ll do differently next time around. Let students know that it’s okay to make mistakes; that mistakes are a welcome opportunity to learn and try something new.

Be Less Helpful

A key to building problem-solving and critical thinking capacities is to be less helpful. Let students figure things out on their own. In classroom implementation, teachers repeatedly share that handing over control to the students and “being less helpful” makes for a big mindshift. This shift is often described as becoming a facilitator, which means knowing when to stand back and knowing when to step-in and offer extra support.

Be Flexible

Recognize that there is no one-size-fits-all answer to any problem. Understanding this and being able to identify unique challenges will help students understand that an initial failure is just a bump in the road. Being flexible also helps students focus on the importance of process over product.

Experts are Everywhere

Experts are everywhere; their differing perspectives and expertise help bring learning to life. But think outside the box about who experts are and integrate multiple opportunities for their involvement. Parents and community members who are not often thought of as experts can speak to life, work, and lived historical experiences. Beyond that, the people usually thought of as experts—researchers, scientists, museum professionals, business professionals, university professors—are more available than many teachers think. It’s often just a matter of asking. And don’t take sole responsibility for finding experts! Seek your students’ help in identifying and securing expert or community support. And when trying to locate experts, don’t forget: students can also be experts.

Maintain a List of Your Support Networks

Some schools have brought the practice of working with the community and outside experts to scale by building databases of parent and community expertise and their interest in working with students. See if a school administrative assistant, student intern, or parent helper can take the lead in developing and maintaining this list for your school community.

Encourage Original Research

Online research is often a great starting point. It can be a way to identify a knowledge base, locate experts, and even find interest-based communities for the topic being approached. While online research is literally right at students’ fingertips, make sure your students spend time offline as well. Original research methods include student-conducted surveys, interviewing experts, and working alongside experts in the field.

This Learning is Open toolkit includes a number of tools and resources that may be helpful as you plan and reflect on your project.

Brainstorming Project Details (Google Presentation) This tool is designed to aid teachers as they brainstorm a project from a variety of starting-points. It’s a helpful tool for independent brainstorming, and would also make a useful workshop tool for teachers who are designing problem-based learning experiences.

Guide to Writing a Problem Statement (PDF) You’ve got to start somewhere. Finding—and defining—a problem is a great place to begin. This guide is a useful tool for teachers and students alike. It will walk you through the process of identifying a problem by providing inspiration on where to look. Then it will support you through the process of defining that problem clearly.

Project Planning Templates (PDF) Need a place to plan out each project phase? Use this project planner to record your ideas in one place. This template is great used alone or in tandem with the other problem-based learning tools.

Ladder of Real World Learning Experiences (PDF) Want to determine if your project is “real” enough? This ladder can be used to help teachers assess their project design based on the real world nature of the project’s learning context, type of activities, and the application of digital tools.

Digital Toolkit (Google Doc) This toolkit was developed in collaboration with teachers and continues to be a community-edited document. The toolkit provides extensive information on digital tools that can be used for planning, brainstorming, collaborating, creating, and sharing work.

Assessing student learning is a crucial part of any dynamic, nonlinear problem-based learning project. Problem-based projects have many parts to them. It’s important to understand each project as a whole as well as each individual component. This section of the toolkit will help you understand problem-based learning assessments and help you develop assessment tools for your problem-based learning experiences.

Because the subject of assessments is so complex, it may be helpful to define how it is approached here.

Portfolio-based Assessment

Each phase of problem-based learning has important tasks and outcomes associated with it. Assessing each phase of the process allows students to receive on-time feedback about their process and associated products and gives them the opportunity to refine and revise their work throughout the process.

Feedback-based Assessment

Problem-based learning emphasizes collaboration with classmates and a range of experts. Assessment should include multiple opportunities for peer feedback, teacher feedback, and expert feedback.

Assessment as a System of Interrelated Feedback Tools

These tools may include rubrics, checklists, observation, portfolios, or quizzes. Whatever the matrix of carefully selected tools, they should optimize the feedback that students receive about what and how they are learning and growing.

Assessment Tools

One way to approach developing assessment tools for your students’ specific problem-based learning project is to deconstruct the learning experience into various categories. Together, these categories make up a simple system through which students may receive feedback on their learning.

Assessing Process

Many students and teachers alike have been conditioned to emphasize and evaluate the end product. While problem-based learning projects often result in impressive end products, it’s important to emphasize each stage of the process with students.

Each phase of problem-based learning process emphasizes important skills, from research and data gathering in the early phases to problem solving, collaboration, and persuasion in the later phases. There are many opportunities to assess student understanding and skill throughout the process. The tools here provide many methods for students to self-assess their process, get feedback from peers, and get feedback from their teachers and other adults.

The Process Portfolio Tool (PDF) provides a place for students to collect their work, define their problem and goals, and reflect throughout the process. Use this as a self-assessment tool, as well as a place to organize the materials for student portfolios.

Driving & Reflection Questioning Guidelines (PDF) is a simple tool for teachers who are integrating problem-based learning into the learning process. The tool highlights the two types of questions teachers/facilitators should consider with students: driving questions and reflection questions. Driving questions push students in their thinking, challenging them to build upon ideas and try new ways to solve problems. Reflection questions ask students to reflect on a process phase once it’s complete, challenging them to think about how they think.

The Peer Feedback Guidelines (PDF) will help students frame how they provide feedback to their peers. The guide includes tips on how and when to use these guidelines in different types of forums (i.e. whole group, gallery-style, and peer-to-peer).

The Buck Institute has also developed a series of rubrics that address various project phases. Their Collaboration Rubric (PDF) can help students be better teammates. (Being an effective teammate is critical to the problem-based learning process.) Their Presentation Rubric (PDF) can help students, adult mentors, and outside experts evaluate final presentations. Final presentations are often one of the most exciting parts of a project.

Assessing Subject Matter and Content

A common concern that emerges in any problem-based learning design is whether projects are able to meet all required subject matter content targets. Because many students are required to learn specific content, there is often tension around the student-directed nature of problem-based learning. While teachers acknowledge that students go deeper into specific content during problem-based learning experiences, teachers also want to ensure that their students are meeting all content goals.

Many teachers in the New Learning Institute’s Model Classroom Program addressed this issue directly by carefully examining their curriculum requirements throughout the planning and implementation phases. Begin by planning activities and real world explorations that address core content. As the project evolves, revisit content standards to mark off and record additional standards met and create a contingency plan for those that have not been addressed.

The Buck Institute’s Rubric for Rubrics (DOC) is an excellent source for designing a rubric to fit your needs. Developing a rubric can be the most simple and effective tool for planning a project around required content targets.

Blended learning is another emerging trend that educators are moving towards as a way to both address individualized skill needs and to create space for real world project strategies, like problem-based learning. In these learning environments, students address skill acquisition through blended experiences and then apply their skills through projects and other real world applications. To learn more about blended models, visit Blend My Learning .

Assessing Mindsets and Skills

In addition to assessing process and subject matter content, it may be helpful to consider the other important mindsets and skills that the problem-based learning project experience fosters. These include persistence, problem solving, collaboration, and adaptability. While problem-based learning supports the development of a large suite of 21st century mindsets and skills, it may be helpful to focus assessments on one or two issues that are most relevant. Some helpful tools may include:

The Buck Institute offers rubrics for Critical Thinking (PDF), Collaboration (PDF), and Creativity and Innovation (PDF) that are aligned to the Common Core State Standards. These can be used as is or tailored to your specific needs.

The Character Growth Card (PDF) from the CharacterLab at Kipp is designed for school assessments more than it is for project assessment, but the list of skills and character traits are relevant to design thinking. With the inclusion of a more relevant, effective scale, these can easily be turned into a rubric, especially when paired with the Buck Institute’s Rubric for Rubrics tool.

Host a Teacher Workshop

Teachers are instrumental in sharing and spreading best practices and innovative strategies to other teachers. Once you’re confident in your conceptual and practical grasp of problem-based learning, share your knowledge and expertise with others.

The downloadable presentation decks below (PowerPoint) are adaptable tools for helping you spread the word to other educators. The presentations vary in length and depth. A 90-minute presentation introduces problem-based learning and provides a hands-on opportunity to complete an activity. The half-day and full day presentations provide in-depth opportunities to explore projects and consider their classroom applications. While this series is structured in a way that each presentation builds on the previous one, each one can also be used individually as appropriate. Each is designed to be interactive and participatory.

Getting Started with Problem-based Learning (PPT) A presentation deck for introducing educators to the Learning is Open problem-based learning process during a 90-minute peer workshop.

Dig Deeper with Problem-based Learning – Half-day (PPT) A presentation deck for training educators on the Learning is Open problem-based learning process during a half-day peer workshop.

Dig Deeper with Problem-based Learning – Full day (PPT) A presentation deck for training educators on the Learning is Open problem-based learning process during a full day peer workshop.

Setting up Learning Experiences Using Real Problems

This New York Times Learning Blog article explores how projects can be set-up with real problems, providing many examples and suggestions for this approach.

Title: “ Guest Lesson | For Authentic Learning Start with Real Problems ” Type: Article Source: Suzie Boss. New York Times Learning Blog

Guest Lesson: Recycling as a Focus for Project-based Learning

There are many ways to set-up a project with a real world problem. This article describes the problem of recycling, providing multiple examples of student projects addressing the issue.

Title: “ Guest Lesson | Recycling as a Focus for Project-Based Learning ” Type: Article Source: Suzie Boss. New York Times Learning Blog

Problem-based Learning: Professional Development Inspires Classroom Project

This video features how the Model Classroom professional development workshop model worked in practice, challenging teachers to collaboratively problem-solve using real world places and experts. It also shows how one workshop participant used her experience to design a yearlong problem-based learning project for first-graders called the “Streamkeepers Project.”

Title: Mission Possible: the Model Classroom Type: Video Source: New Learning Institute

Problem-based Learning in an Engineering Class: Solutions to a Water Shortage

Engineering teacher Bryan Coburn used the problem of a local water shortage to inspire his students to conduct research and design solutions.

Title: “ National Project Aims to Inspire the Model Classroom ” Type: Article Source: eSchool News

Making Project-based Learning More Meaningful

This article provides great tips on how to design projects to be relevant and purposeful for students. While it addresses the larger umbrella of project-based learning, the suggestions and tips provided apply to problem-based learning.

Title: “ How to Reinvent Project-Based Learning to Make it More Meaningful ” Type: Article Source: KQED Mindshift

PBL Downloads

Guide to Writing a Problem Statement (PDF)

A walk-through guide for identifying and defining a problem.

Project Planning Templates (PDF)

A planning template for standalone use or to be used along with other problem-based learning tools.

Process Portfolio Tool (PDF)

A self-assessment tool to support students as they collect their work, define their problem and goals, and make reflections throughout the process.

More PBL Downloads

Getting Started with Problem-based Learning (PPT)

A presentation deck for introducing educators to the Project MASH problem-based learning process during a 90-minute peer workshop.

Dig Deeper with Problem-based Learning – Half-day (PPT)

A presentation deck for training educators on the PBL process during a half-day peer workshop.

Dig Deeper with Problem-based Learning – Full day (PPT)

A presentation deck for training educators on the PBL process during a full day peer workshop.

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The Problem-Solving Process

Looking at the basic problem-solving process to help keep you on the right track.

By the Mind Tools Content Team

Problem-solving is an important part of planning and decision-making. The process has much in common with the decision-making process, and in the case of complex decisions, can form part of the process itself.

We face and solve problems every day, in a variety of guises and of differing complexity. Some, such as the resolution of a serious complaint, require a significant amount of time, thought and investigation. Others, such as a printer running out of paper, are so quickly resolved they barely register as a problem at all.

Despite the everyday occurrence of problems, many people lack confidence when it comes to solving them, and as a result may chose to stay with the status quo rather than tackle the issue. Broken down into steps, however, the problem-solving process is very simple. While there are many tools and techniques available to help us solve problems, the outline process remains the same.

The main stages of problem-solving are outlined below, though not all are required for every problem that needs to be solved.

1. Define the Problem

Clarify the problem before trying to solve it. A common mistake with problem-solving is to react to what the problem appears to be, rather than what it actually is. Write down a simple statement of the problem, and then underline the key words. Be certain there are no hidden assumptions in the key words you have underlined. One way of doing this is to use a synonym to replace the key words. For example, ‘We need to encourage higher productivity ’ might become ‘We need to promote superior output ’ which has a different meaning.

2. Analyze the Problem

Ask yourself, and others, the following questions.

Where is the problem occurring?
When is it occurring?
Why is it happening?

Be careful not to jump to ‘who is causing the problem?’. When stressed and faced with a problem it is all too easy to assign blame. This, however, can cause negative feeling and does not help to solve the problem. As an example, if an employee is underperforming, the root of the problem might lie in a number of areas, such as lack of training, workplace bullying or management style. To assign immediate blame to the employee would not therefore resolve the underlying issue.

Once the answers to the where, when and why have been determined, the following questions should also be asked:

Where can further information be found?
Is this information correct, up-to-date and unbiased?
What does this information mean in terms of the available options?

3. Generate Potential Solutions

When generating potential solutions it can be a good idea to have a mixture of ‘right brain’ and ‘left brain’ thinkers. In other words, some people who think laterally and some who think logically. This provides a balance in terms of generating the widest possible variety of solutions while also being realistic about what can be achieved. There are many tools and techniques which can help produce solutions, including thinking about the problem from a number of different perspectives, and brainstorming, where a team or individual write as many possibilities as they can think of to encourage lateral thinking and generate a broad range of potential solutions.

4. Select Best Solution

When selecting the best solution, consider:

Is this a long-term solution, or a ‘quick fix’?
Is the solution achievable in terms of available resources and time?
Are there any risks associated with the chosen solution?
Could the solution, in itself, lead to other problems?

This stage in particular demonstrates why problem-solving and decision-making are so closely related.

5. Take Action

In order to implement the chosen solution effectively, consider the following:

What will the situation look like when the problem is resolved?
What needs to be done to implement the solution? Are there systems or processes that need to be adjusted?
What will be the success indicators?
What are the timescales for the implementation? Does the scale of the problem/implementation require a project plan?
Who is responsible?

Once the answers to all the above questions are written down, they can form the basis of an action plan.

6. Monitor and Review

One of the most important factors in successful problem-solving is continual observation and feedback. Use the success indicators in the action plan to monitor progress on a regular basis. Is everything as expected? Is everything on schedule? Keep an eye on priorities and timelines to prevent them from slipping.

If the indicators are not being met, or if timescales are slipping, consider what can be done. Was the plan realistic? If so, are sufficient resources being made available? Are these resources targeting the correct part of the plan? Or does the plan need to be amended? Regular review and discussion of the action plan is important so small adjustments can be made on a regular basis to help keep everything on track.

Once all the indicators have been met and the problem has been resolved, consider what steps can now be taken to prevent this type of problem recurring? It may be that the chosen solution already prevents a recurrence, however if an interim or partial solution has been chosen it is important not to lose momentum.

Problems, by their very nature, will not always fit neatly into a structured problem-solving process. This process, therefore, is designed as a framework which can be adapted to individual needs and nature.

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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Strategy to beat the odds

Five routes to more innovative problem solving

Problem based learning: a teacher's guide

December 10, 2021

Find out how teachers use problem-based learning models to improve engagement and drive attainment.

Main, P (2021, December 10). Problem based learning: a teacher's guide. Retrieved from https://www.structural-learning.com/post/problem-based-learning-a-teachers-guide

What is problem-based learning?

Problem-based learning (PBL) is a style of teaching that encourages students to become the drivers of their learning process . Problem-based learning involves complex learning issues from real-world problems and makes them the classroom's topic of discussion ; encouraging students to understand concepts through problem-solving skills rather than simply learning facts. When schools find time in the curriculum for this style of teaching it offers students an authentic vehicle for the integration of knowledge .

Embracing this pedagogical approach enables schools to balance subject knowledge acquisition with a skills agenda . Often used in medical education, this approach has equal significance in mainstream education where pupils can apply their knowledge to real-life problems.

PBL is not only helpful in learning course content , but it can also promote the development of problem-solving abilities , critical thinking skills , and communication skills while providing opportunities to work in groups , find and analyse research materials , and take part in life-long learning .

PBL is a student-centred teaching method in which students understand a topic by working in groups. They work out an open-ended problem , which drives the motivation to learn. These sorts of theories of teaching do require schools to invest time and resources into supporting self-directed learning. Not all curriculum knowledge is best acquired through this process, rote learning still has its place in certain situations. In this article, we will look at how we can equip our students to take more ownership of the learning process and utilise more sophisticated ways for the integration of knowledge .

Philosophical Underpinnings of PBL

Problem-Based Learning (PBL), with its roots in the philosophies of John Dewey, Maria Montessori, and Jerome Bruner, aligns closely with the social constructionist view of learning. This approach positions learners as active participants in the construction of knowledge, contrasting with traditional models of instruction where learners are seen as passive recipients of information.

Dewey, a seminal figure in progressive education, advocated for active learning and real-world problem-solving, asserting that learning is grounded in experience and interaction. In PBL, learners tackle complex, real-world problems, which mirrors Dewey's belief in the interconnectedness of education and practical life.

Montessori also endorsed learner-centric, self-directed learning, emphasizing the child's potential to construct their own learning experiences. This parallels with PBL’s emphasis on self-directed learning, where students take ownership of their learning process.

Jerome Bruner’s theories underscored the idea of learning as an active, social process. His concept of a 'spiral curriculum' – where learning is revisited in increasing complexity – can be seen reflected in the iterative problem-solving process in PBL.

Webb’s Depth of Knowledge (DOK) framework aligns with PBL as it encourages higher-order cognitive skills. The complex tasks in PBL often demand analytical and evaluative skills (Webb's DOK levels 3 and 4) as students engage with the problem, devise a solution, and reflect on their work.

The effectiveness of PBL is supported by psychological theories like the information processing theory, which highlights the role of active engagement in enhancing memory and recall. A study by Strobel and Van Barneveld (2009) found that PBL students show improved retention of knowledge, possibly due to the deep cognitive processing involved.

As cognitive scientist Daniel Willingham aptly puts it, "Memory is the residue of thought." PBL encourages learners to think critically and deeply, enhancing both learning and retention.

Here's a quick overview:

John Dewey : Emphasized learning through experience and the importance of problem-solving.
Maria Montessori : Advocated for child-centered, self-directed learning.
Jerome Bruner : Underlined learning as a social process and proposed the spiral curriculum.
Webb’s DOK : Supports PBL's encouragement of higher-order thinking skills.
Information Processing Theory : Reinforces the notion that active engagement in PBL enhances memory and recall.

This deep-rooted philosophical and psychological framework strengthens the validity of the problem-based learning approach, confirming its beneficial role in promoting valuable cognitive skills and fostering positive student learning outcomes.

What are the characteristics of problem-based learning?

Adding a little creativity can change a topic into a problem-based learning activity. The following are some of the characteristics of a good PBL model:

The problem encourages students to search for a deeper understanding of content knowledge;
Students are responsible for their learning. PBL has a student-centred learning approach . Students' motivation increases when responsibility for the process and solution to the problem rests with the learner;
The problem motivates pupils to gain desirable learning skills and to defend well-informed decisions ;
The problem connects the content learning goals with the previous knowledge. PBL allows students to access, integrate and study information from multiple disciplines that might relate to understanding and resolving a specific problem—just as persons in the real world recollect and use the application of knowledge that they have gained from diverse sources in their life.
In a multistage project, the first stage of the problem must be engaging and open-ended to make students interested in the problem. In the real world, problems are poorly-structured. Research suggests that well-structured problems make students less invested and less motivated in the development of the solution. The problem simulations used in problem-based contextual learning are less structured to enable students to make a free inquiry.

In a group project, the problem must have some level of complexity that motivates students towards knowledge acquisition and to work together for finding the solution. PBL involves collaboration between learners. In professional life, most people will find themselves in employment where they would work productively and share information with others. PBL leads to the development of such essential skills . In a PBL session, the teacher would ask questions to make sure that knowledge has been shared between pupils;
At the end of each problem or PBL, self and peer assessments are performed. The main purpose of assessments is to sharpen a variety of metacognitive processing skills and to reinforce self-reflective learning.
Student assessments would evaluate student progress towards the objectives of problem-based learning. The learning goals of PBL are both process-based and knowledge-based. Students must be assessed on both these dimensions to ensure that they are prospering as intended from the PBL approach. Students must be able to identify and articulate what they understood and what they learned.

Why is Problem-based learning a significant skill?

Using Problem-Based Learning across a school promotes critical competence, inquiry , and knowledge application in social, behavioural and biological sciences. Practice-based learning holds a strong track record of successful learning outcomes in higher education settings such as graduates of Medical Schools.

Educational models using PBL can improve learning outcomes by teaching students how to implement theory into practice and build problem-solving skills. For example, within the field of health sciences education, PBL makes the learning process for nurses and medical students self-centred and promotes their teamwork and leadership skills. Within primary and secondary education settings, this model of teaching, with the right sort of collaborative tools , can advance the wider skills development valued in society.

At Structural Learning, we have been developing a self-assessment tool designed to monitor the progress of children. Utilising these types of teaching theories curriculum wide can help a school develop the learning behaviours our students will need in the workplace.

Curriculum wide collaborative tools include Writers Block and the Universal Thinking Framework . Along with graphic organisers, these tools enable children to collaborate and entertain different perspectives that they might not otherwise see. Putting learning in action by using the block building methodology enables children to reach their learning goals by experimenting and iterating.

Scaffolding problem based learning with classroom tools

How is problem-based learning different from inquiry-based learning?

The major difference between inquiry-based learning and PBL relates to the role of the teacher . In the case of inquiry-based learning, the teacher is both a provider of classroom knowledge and a facilitator of student learning (expecting/encouraging higher-order thinking). On the other hand, PBL is a deep learning approach, in which the teacher is the supporter of the learning process and expects students to have clear thinking, but the teacher is not the provider of classroom knowledge about the problem—the responsibility of providing information belongs to the learners themselves.

As well as being used systematically in medical education, this approach has significant implications for integrating learning skills into mainstream classrooms .

Using a critical thinking disposition inventory, schools can monitor the wider progress of their students as they apply their learning skills across the traditional curriculum. Authentic problems call students to apply their critical thinking abilities in new and purposeful ways. As students explain their ideas to one another, they develop communication skills that might not otherwise be nurtured.

Depending on the curriculum being delivered by a school, there may well be an emphasis on building critical thinking abilities in the classroom. Within the International Baccalaureate programs, these life-long skills are often cited in the IB learner profile . Critical thinking dispositions are highly valued in the workplace and this pedagogical approach can be used to harness these essential 21st-century skills.

What are the Benefits of Problem-Based Learning?

Student-led Problem-Based Learning is one of the most useful ways to make students drivers of their learning experience. It makes students creative, innovative, logical and open-minded. The educational practice of Problem-Based Learning also provides opportunities for self-directed and collaborative learning with others in an active learning and hands-on process. Below are the most significant benefits of problem-based learning processes:

Self-learning: As a self-directed learning method, problem-based learning encourages children to take responsibility and initiative for their learning processes . As children use creativity and research, they develop skills that will help them in their adulthood.
Engaging : Students don't just listen to the teacher, sit back and take notes. Problem-based learning processes encourages students to take part in learning activities, use learning resources , stay active , think outside the box and apply critical thinking skills to solve problems.
Teamwork : Most of the problem-based learning issues involve students collaborative learning to find a solution. The educational practice of PBL builds interpersonal skills, listening and communication skills and improves the skills of collaboration and compromise.
Intrinsic Rewards: In most problem-based learning projects, the reward is much bigger than good grades. Students gain the pride and satisfaction of finding an innovative solution, solving a riddle, or creating a tangible product.
Transferable Skills: The acquisition of knowledge through problem-based learning strategies don't just help learners in one class or a single subject area. Students can apply these skills to a plethora of subject matter as well as in real life.
Multiple Learning Opportunities : A PBL model offers an open-ended problem-based acquisition of knowledge, which presents a real-world problem and asks learners to come up with well-constructed responses. Students can use multiple sources such as they can access online resources, using their prior knowledge, and asking momentous questions to brainstorm and come up with solid learning outcomes. Unlike traditional approaches , there might be more than a single right way to do something, but this process motivates learners to explore potential solutions whilst staying active.

Solving authentic problems using problem based learning

Embracing problem-based learning

Problem-based learning can be seen as a deep learning approach and when implemented effectively as part of a broad and balanced curriculum , a successful teaching strategy in education. PBL has a solid epistemological and philosophical foundation and a strong track record of success in multiple areas of study. Learners must experience problem-based learning methods and engage in positive solution-finding activities. PBL models allow learners to gain knowledge through real-world problems, which offers more strength to their understanding and helps them find the connection between classroom learning and the real world at large.

As they solve problems, students can evolve as individuals and team-mates. One word of caution, not all classroom tasks will lend themselves to this learning theory. Take spellings , for example, this is usually delivered with low-stakes quizzing through a practice-based learning model. PBL allows students to apply their knowledge creatively but they need to have a certain level of background knowledge to do this, rote learning might still have its place after all.

Key Concepts and considerations for school leaders

1. Problem Based Learning (PBL)

Problem-based learning (PBL) is an educational method that involves active student participation in solving authentic problems. Students are given a task or question that they must answer using their prior knowledge and resources. They then collaborate with each other to come up with solutions to the problem. This collaborative effort leads to deeper learning than traditional lectures or classroom instruction .

Key question: Inside a traditional curriculum , what opportunities across subject areas do you immediately see?

2. Deep Learning

Deep learning is a term used to describe the ability to learn concepts deeply. For example, if you were asked to memorize a list of numbers, you would probably remember the first five numbers easily, but the last number would be difficult to recall. However, if you were taught to understand the concept behind the numbers, you would be able to remember the last number too.

Key question: How will you make sure that students use a full range of learning styles and learning skills ?

3. Epistemology

Epistemology is the branch of philosophy that deals with the nature of knowledge . It examines the conditions under which something counts as knowledge.

Key question: As well as focusing on critical thinking dispositions, what subject knowledge should the students understand?

4. Philosophy

Philosophy is the study of general truths about human life. Philosophers examine questions such as “What makes us happy?”, “How should we live our lives?”, and “Why does anything exist?”

Key question: Are there any opportunities for embracing philosophical enquiry into the project to develop critical thinking abilities ?

5. Curriculum

A curriculum is a set of courses designed to teach specific subjects. These courses may include mathematics , science, social studies, language arts, etc.

Key question: How will subject leaders ensure that the integrity of the curriculum is maintained?

6. Broad and Balanced Curriculum

Broad and balanced curricula are those that cover a wide range of topics. Some examples of these types of curriculums include AP Biology, AP Chemistry, AP English Language, AP Physics 1, AP Psychology , AP Spanish Literature, AP Statistics, AP US History, AP World History, IB Diploma Programme, IB Primary Years Program, IB Middle Years Program, IB Diploma Programme .

Key question: Are the teachers who have identified opportunities for a problem-based curriculum?

7. Successful Teaching Strategy

Successful teaching strategies involve effective communication techniques, clear objectives, and appropriate assessments. Teachers must ensure that their lessons are well-planned and organized. They must also provide opportunities for students to interact with one another and share information.

Key question: What pedagogical approaches and teaching strategies will you use?

8. Positive Solution Finding

Positive solution finding is a type of problem-solving where students actively seek out answers rather than passively accept what others tell them.

Key question: How will you ensure your problem-based curriculum is met with a positive mindset from students and teachers?

9. Real World Application

Real-world application refers to applying what students have learned in class to situations that occur in everyday life.

Key question: Within your local school community , are there any opportunities to apply knowledge and skills to real-life problems?

10. Creativity

Creativity is the ability to think of ideas that no one else has thought of yet. Creative thinking requires divergent thinking, which means thinking in different directions.

Key question: What teaching techniques will you use to enable children to generate their own ideas ?

11. Teamwork

Teamwork is the act of working together towards a common goal. Teams often consist of two or more people who work together to achieve a shared objective.

Key question: What opportunities are there to engage students in dialogic teaching methods where they talk their way through the problem?

12. Knowledge Transfer

Knowledge transfer occurs when teachers use their expertise to help students develop skills and abilities .

Key question: Can teachers be able to track the success of the project using improvement scores?

13. Active Learning

Active learning is any form of instruction that engages students in the learning process. Examples of active learning include group discussions, role-playing, debates, presentations, and simulations .

Key question: Will there be an emphasis on learning to learn and developing independent learning skills ?

14. Student Engagement

Student engagement is the degree to which students feel motivated to participate in academic activities.

Key question: Are there any tools available to monitor student engagement during the problem-based curriculum ?

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Classroom Practice

How to improve your problem solving skills and build effective problem solving strategies

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What is a problem solving process?

What are the problem solving steps I need to follow?

Problem solving strategies

What skills do i need to be an effective problem solver, how can i improve my problem solving skills.

Solving problems is like baking a cake. You can go straight into the kitchen without a recipe or the right ingredients and do your best, but the end result is unlikely to be very tasty!

Using a process to bake a cake allows you to use the best ingredients without waste, collect the right tools, account for allergies, decide whether it is a birthday or wedding cake, and then bake efficiently and on time. The result is a better cake that is fit for purpose, tastes better and has created less mess in the kitchen. Also, it should have chocolate sprinkles. Having a step by step process to solve organizational problems allows you to go through each stage methodically and ensure you are trying to solve the right problems and select the most appropriate, effective solutions.

What are the problem solving steps I need to follow?

All problem solving processes go through a number of steps in order to move from identifying a problem to resolving it.

Depending on your problem solving model and who you ask, there can be anything between four and nine problem solving steps you should follow in order to find the right solution. Whatever framework you and your group use, there are some key items that should be addressed in order to have an effective process.

We’ve looked at problem solving processes from sources such as the American Society for Quality and their four step approach , and Mediate ‘s six step process. By reflecting on those and our own problem solving processes, we’ve come up with a sequence of seven problem solving steps we feel best covers everything you need in order to effectively solve problems.

1. Problem identification

The first stage of any problem solving process is to identify the problem or problems you might want to solve. Effective problem solving strategies always begin by allowing a group scope to articulate what they believe the problem to be and then coming to some consensus over which problem they approach first. Problem solving activities used at this stage often have a focus on creating frank, open discussion so that potential problems can be brought to the surface.

2. Problem analysis

Though this step is not a million miles from problem identification, problem analysis deserves to be considered separately. It can often be an overlooked part of the process and is instrumental when it comes to developing effective solutions.

The process of problem analysis means ensuring that the problem you are seeking to solve is the right problem . As part of this stage, you may look deeper and try to find the root cause of a specific problem at a team or organizational level.

Remember that problem solving strategies should not only be focused on putting out fires in the short term but developing long term solutions that deal with the root cause of organizational challenges.

Whatever your approach, analyzing a problem is crucial in being able to select an appropriate solution and the problem solving skills deployed in this stage are beneficial for the rest of the process and ensuring the solutions you create are fit for purpose.

3. Solution generation

Once your group has nailed down the particulars of the problem you wish to solve, you want to encourage a free flow of ideas connecting to solving that problem. This can take the form of problem solving games that encourage creative thinking or problem solving activities designed to produce working prototypes of possible solutions.

The key to ensuring the success of this stage of the problem solving process is to encourage quick, creative thinking and create an open space where all ideas are considered. The best solutions can come from unlikely places and by using problem solving techniques that celebrate invention, you might come up with solution gold.

4. Solution development

No solution is likely to be perfect right out of the gate. It’s important to discuss and develop the solutions your group has come up with over the course of following the previous problem solving steps in order to arrive at the best possible solution. Problem solving games used in this stage involve lots of critical thinking, measuring potential effort and impact, and looking at possible solutions analytically.

During this stage, you will often ask your team to iterate and improve upon your frontrunning solutions and develop them further. Remember that problem solving strategies always benefit from a multitude of voices and opinions, and not to let ego get involved when it comes to choosing which solutions to develop and take further.

Finding the best solution is the goal of all problem solving workshops and here is the place to ensure that your solution is well thought out, sufficiently robust and fit for purpose.

5. Decision making

Nearly there! Once your group has reached consensus and selected a solution that applies to the problem at hand you have some decisions to make. You will want to work on allocating ownership of the project, figure out who will do what, how the success of the solution will be measured and decide the next course of action.

The decision making stage is a part of the problem solving process that can get missed or taken as for granted. Fail to properly allocate roles and plan out how a solution will actually be implemented and it less likely to be successful in solving the problem.

Have clear accountabilities, actions, timeframes, and follow-ups. Make these decisions and set clear next-steps in the problem solving workshop so that everyone is aligned and you can move forward effectively as a group.

Ensuring that you plan for the roll-out of a solution is one of the most important problem solving steps. Without adequate planning or oversight, it can prove impossible to measure success or iterate further if the problem was not solved.

6. Solution implementation

This is what we were waiting for! All problem solving strategies have the end goal of implementing a solution and solving a problem in mind.

Remember that in order for any solution to be successful, you need to help your group through all of the previous problem solving steps thoughtfully. Only then can you ensure that you are solving the right problem but also that you have developed the correct solution and can then successfully implement and measure the impact of that solution.

Project management and communication skills are key here – your solution may need to adjust when out in the wild or you might discover new challenges along the way.

7. Solution evaluation

So you and your team developed a great solution to a problem and have a gut feeling its been solved. Work done, right? Wrong. All problem solving strategies benefit from evaluation, consideration, and feedback. You might find that the solution does not work for everyone, might create new problems, or is potentially so successful that you will want to roll it out to larger teams or as part of other initiatives.

None of that is possible without taking the time to evaluate the success of the solution you developed in your problem solving model and adjust if necessary.

Remember that the problem solving process is often iterative and it can be common to not solve complex issues on the first try. Even when this is the case, you and your team will have generated learning that will be important for future problem solving workshops or in other parts of the organization.

It’s worth underlining how important record keeping is throughout the problem solving process. If a solution didn’t work, you need to have the data and records to see why that was the case. If you go back to the drawing board, notes from the previous workshop can help save time. Data and insight is invaluable at every stage of the problem solving process and this one is no different.

Problem solving workshops made easy

Problem solving strategies are methods of approaching and facilitating the process of problem-solving with a set of techniques , actions, and processes. Different strategies are more effective if you are trying to solve broad problems such as achieving higher growth versus more focused problems like, how do we improve our customer onboarding process?

Broadly, the problem solving steps outlined above should be included in any problem solving strategy though choosing where to focus your time and what approaches should be taken is where they begin to differ. You might find that some strategies ask for the problem identification to be done prior to the session or that everything happens in the course of a one day workshop.

The key similarity is that all good problem solving strategies are structured and designed. Four hours of open discussion is never going to be as productive as a four-hour workshop designed to lead a group through a problem solving process.

Good problem solving strategies are tailored to the team, organization and problem you will be attempting to solve. Here are some example problem solving strategies you can learn from or use to get started.

Use a workshop to lead a team through a group process

Often, the first step to solving problems or organizational challenges is bringing a group together effectively. Most teams have the tools, knowledge, and expertise necessary to solve their challenges – they just need some guidance in how to use leverage those skills and a structure and format that allows people to focus their energies.

Facilitated workshops are one of the most effective ways of solving problems of any scale. By designing and planning your workshop carefully, you can tailor the approach and scope to best fit the needs of your team and organization.

Problem solving workshop

Creating a bespoke, tailored process
Tackling problems of any size
Building in-house workshop ability and encouraging their use

Workshops are an effective strategy for solving problems. By using tried and test facilitation techniques and methods, you can design and deliver a workshop that is perfectly suited to the unique variables of your organization. You may only have the capacity for a half-day workshop and so need a problem solving process to match.

By using our session planner tool and importing methods from our library of 700+ facilitation techniques, you can create the right problem solving workshop for your team. It might be that you want to encourage creative thinking or look at things from a new angle to unblock your groups approach to problem solving. By tailoring your workshop design to the purpose, you can help ensure great results.

One of the main benefits of a workshop is the structured approach to problem solving. Not only does this mean that the workshop itself will be successful, but many of the methods and techniques will help your team improve their working processes outside of the workshop.

We believe that workshops are one of the best tools you can use to improve the way your team works together. Start with a problem solving workshop and then see what team building, culture or design workshops can do for your organization!

Run a design sprint

Great for:

aligning large, multi-discipline teams
quickly designing and testing solutions
tackling large, complex organizational challenges and breaking them down into smaller tasks

By using design thinking principles and methods, a design sprint is a great way of identifying, prioritizing and prototyping solutions to long term challenges that can help solve major organizational problems with quick action and measurable results.

Some familiarity with design thinking is useful, though not integral, and this strategy can really help a team align if there is some discussion around which problems should be approached first.

The stage-based structure of the design sprint is also very useful for teams new to design thinking. The inspiration phase, where you look to competitors that have solved your problem, and the rapid prototyping and testing phases are great for introducing new concepts that will benefit a team in all their future work.

It can be common for teams to look inward for solutions and so looking to the market for solutions you can iterate on can be very productive. Instilling an agile prototyping and testing mindset can also be great when helping teams move forwards – generating and testing solutions quickly can help save time in the long run and is also pretty exciting!

Break problems down into smaller issues

Organizational challenges and problems are often complicated and large scale in nature. Sometimes, trying to resolve such an issue in one swoop is simply unachievable or overwhelming. Try breaking down such problems into smaller issues that you can work on step by step. You may not be able to solve the problem of churning customers off the bat, but you can work with your team to identify smaller effort but high impact elements and work on those first.

This problem solving strategy can help a team generate momentum, prioritize and get some easy wins. It’s also a great strategy to employ with teams who are just beginning to learn how to approach the problem solving process. If you want some insight into a way to employ this strategy, we recommend looking at our design sprint template below!

Use guiding frameworks or try new methodologies

Some problems are best solved by introducing a major shift in perspective or by using new methodologies that encourage your team to think differently.

Props and tools such as Methodkit , which uses a card-based toolkit for facilitation, or Lego Serious Play can be great ways to engage your team and find an inclusive, democratic problem solving strategy. Remember that play and creativity are great tools for achieving change and whatever the challenge, engaging your participants can be very effective where other strategies may have failed.

LEGO Serious Play

Improving core problem solving skills
Thinking outside of the box
Encouraging creative solutions

LEGO Serious Play is a problem solving methodology designed to get participants thinking differently by using 3D models and kinesthetic learning styles. By physically building LEGO models based on questions and exercises, participants are encouraged to think outside of the box and create their own responses.

Collaborate LEGO Serious Play exercises are also used to encourage communication and build problem solving skills in a group. By using this problem solving process, you can often help different kinds of learners and personality types contribute and unblock organizational problems with creative thinking.

Problem solving strategies like LEGO Serious Play are super effective at helping a team solve more skills-based problems such as communication between teams or a lack of creative thinking. Some problems are not suited to LEGO Serious Play and require a different problem solving strategy.

Card Decks and Method Kits

New facilitators or non-facilitators
Approaching difficult subjects with a simple, creative framework
Engaging those with varied learning styles

Card decks and method kids are great tools for those new to facilitation or for whom facilitation is not the primary role. Card decks such as the emotional culture deck can be used for complete workshops and in many cases, can be used right out of the box. Methodkit has a variety of kits designed for scenarios ranging from personal development through to personas and global challenges so you can find the right deck for your particular needs.

Having an easy to use framework that encourages creativity or a new approach can take some of the friction or planning difficulties out of the workshop process and energize a team in any setting. Simplicity is the key with these methods. By ensuring everyone on your team can get involved and engage with the process as quickly as possible can really contribute to the success of your problem solving strategy.

Source external advice

Looking to peers, experts and external facilitators can be a great way of approaching the problem solving process. Your team may not have the necessary expertise, insights of experience to tackle some issues, or you might simply benefit from a fresh perspective. Some problems may require bringing together an entire team, and coaching managers or team members individually might be the right approach. Remember that not all problems are best resolved in the same manner.

If you’re a solo entrepreneur, peer groups, coaches and mentors can also be invaluable at not only solving specific business problems, but in providing a support network for resolving future challenges. One great approach is to join a Mastermind Group and link up with like-minded individuals and all grow together. Remember that however you approach the sourcing of external advice, do so thoughtfully, respectfully and honestly. Reciprocate where you can and prepare to be surprised by just how kind and helpful your peers can be!

Mastermind Group

Solo entrepreneurs or small teams with low capacity
Peer learning and gaining outside expertise
Getting multiple external points of view quickly

Problem solving in large organizations with lots of skilled team members is one thing, but how about if you work for yourself or in a very small team without the capacity to get the most from a design sprint or LEGO Serious Play session?

A mastermind group – sometimes known as a peer advisory board – is where a group of people come together to support one another in their own goals, challenges, and businesses. Each participant comes to the group with their own purpose and the other members of the group will help them create solutions, brainstorm ideas, and support one another.

Mastermind groups are very effective in creating an energized, supportive atmosphere that can deliver meaningful results. Learning from peers from outside of your organization or industry can really help unlock new ways of thinking and drive growth. Access to the experience and skills of your peers can be invaluable in helping fill the gaps in your own ability, particularly in young companies.

A mastermind group is a great solution for solo entrepreneurs, small teams, or for organizations that feel that external expertise or fresh perspectives will be beneficial for them. It is worth noting that Mastermind groups are often only as good as the participants and what they can bring to the group. Participants need to be committed, engaged and understand how to work in this context.

Coaching and mentoring

Focused learning and development
Filling skills gaps
Working on a range of challenges over time

Receiving advice from a business coach or building a mentor/mentee relationship can be an effective way of resolving certain challenges. The one-to-one format of most coaching and mentor relationships can really help solve the challenges those individuals are having and benefit the organization as a result.

A great mentor can be invaluable when it comes to spotting potential problems before they arise and coming to understand a mentee very well has a host of other business benefits. You might run an internal mentorship program to help develop your team’s problem solving skills and strategies or as part of a large learning and development program. External coaches can also be an important part of your problem solving strategy, filling skills gaps for your management team or helping with specific business issues.

Now we’ve explored the problem solving process and the steps you will want to go through in order to have an effective session, let’s look at the skills you and your team need to be more effective problem solvers.

Problem solving skills are highly sought after, whatever industry or team you work in. Organizations are keen to employ people who are able to approach problems thoughtfully and find strong, realistic solutions. Whether you are a facilitator , a team leader or a developer, being an effective problem solver is a skill you’ll want to develop.

Problem solving skills form a whole suite of techniques and approaches that an individual uses to not only identify problems but to discuss them productively before then developing appropriate solutions.

Here are some of the most important problem solving skills everyone from executives to junior staff members should learn. We’ve also included an activity or exercise from the SessionLab library that can help you and your team develop that skill.

If you’re running a workshop or training session to try and improve problem solving skills in your team, try using these methods to supercharge your process!

Active listening

Active listening is one of the most important skills anyone who works with people can possess. In short, active listening is a technique used to not only better understand what is being said by an individual, but also to be more aware of the underlying message the speaker is trying to convey. When it comes to problem solving, active listening is integral for understanding the position of every participant and to clarify the challenges, ideas and solutions they bring to the table.

Some active listening skills include:

Paying complete attention to the speaker.
Removing distractions.
Avoid interruption.
Taking the time to fully understand before preparing a rebuttal.
Responding respectfully and appropriately.
Demonstrate attentiveness and positivity with an open posture, making eye contact with the speaker, smiling and nodding if appropriate. Show that you are listening and encourage them to continue.
Be aware of and respectful of feelings. Judge the situation and respond appropriately. You can disagree without being disrespectful.
Observe body language.
Paraphrase what was said in your own words, either mentally or verbally.
Remain neutral.
Reflect and take a moment before responding.
Ask deeper questions based on what is said and clarify points where necessary.

Active Listening #hyperisland #skills #active listening #remote-friendly This activity supports participants to reflect on a question and generate their own solutions using simple principles of active listening and peer coaching. It’s an excellent introduction to active listening but can also be used with groups that are already familiar with it. Participants work in groups of three and take turns being: “the subject”, the listener, and the observer.

Analytical skills

All problem solving models require strong analytical skills, particularly during the beginning of the process and when it comes to analyzing how solutions have performed.

Analytical skills are primarily focused on performing an effective analysis by collecting, studying and parsing data related to a problem or opportunity.

It often involves spotting patterns, being able to see things from different perspectives and using observable facts and data to make suggestions or produce insight.

Analytical skills are also important at every stage of the problem solving process and by having these skills, you can ensure that any ideas or solutions you create or backed up analytically and have been sufficiently thought out.

Nine Whys #innovation #issue analysis #liberating structures With breathtaking simplicity, you can rapidly clarify for individuals and a group what is essentially important in their work. You can quickly reveal when a compelling purpose is missing in a gathering and avoid moving forward without clarity. When a group discovers an unambiguous shared purpose, more freedom and more responsibility are unleashed. You have laid the foundation for spreading and scaling innovations with fidelity.

Collaboration

Trying to solve problems on your own is difficult. Being able to collaborate effectively, with a free exchange of ideas, to delegate and be a productive member of a team is hugely important to all problem solving strategies.

Remember that whatever your role, collaboration is integral, and in a problem solving process, you are all working together to find the best solution for everyone.

Marshmallow challenge with debriefing #teamwork #team #leadership #collaboration In eighteen minutes, teams must build the tallest free-standing structure out of 20 sticks of spaghetti, one yard of tape, one yard of string, and one marshmallow. The marshmallow needs to be on top. The Marshmallow Challenge was developed by Tom Wujec, who has done the activity with hundreds of groups around the world. Visit the Marshmallow Challenge website for more information. This version has an extra debriefing question added with sample questions focusing on roles within the team.

Communication

Being an effective communicator means being empathetic, clear and succinct, asking the right questions, and demonstrating active listening skills throughout any discussion or meeting.

In a problem solving setting, you need to communicate well in order to progress through each stage of the process effectively. As a team leader, it may also fall to you to facilitate communication between parties who may not see eye to eye. Effective communication also means helping others to express themselves and be heard in a group.

Bus Trip #feedback #communication #appreciation #closing #thiagi #team This is one of my favourite feedback games. I use Bus Trip at the end of a training session or a meeting, and I use it all the time. The game creates a massive amount of energy with lots of smiles, laughs, and sometimes even a teardrop or two.

Creative problem solving skills can be some of the best tools in your arsenal. Thinking creatively, being able to generate lots of ideas and come up with out of the box solutions is useful at every step of the process.

The kinds of problems you will likely discuss in a problem solving workshop are often difficult to solve, and by approaching things in a fresh, creative manner, you can often create more innovative solutions.

Having practical creative skills is also a boon when it comes to problem solving. If you can help create quality design sketches and prototypes in record time, it can help bring a team to alignment more quickly or provide a base for further iteration.

The paper clip method #sharing #creativity #warm up #idea generation #brainstorming The power of brainstorming. A training for project leaders, creativity training, and to catalyse getting new solutions.

Critical thinking

Critical thinking is one of the fundamental problem solving skills you’ll want to develop when working on developing solutions. Critical thinking is the ability to analyze, rationalize and evaluate while being aware of personal bias, outlying factors and remaining open-minded.

Defining and analyzing problems without deploying critical thinking skills can mean you and your team go down the wrong path. Developing solutions to complex issues requires critical thinking too – ensuring your team considers all possibilities and rationally evaluating them.

Agreement-Certainty Matrix #issue analysis #liberating structures #problem solving You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic . A problem is simple when it can be solved reliably with practices that are easy to duplicate. It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably. A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail. Chaotic is when the context is too turbulent to identify a path forward. A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.” The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Data analysis

Though it shares lots of space with general analytical skills, data analysis skills are something you want to cultivate in their own right in order to be an effective problem solver.

Being good at data analysis doesn’t just mean being able to find insights from data, but also selecting the appropriate data for a given issue, interpreting it effectively and knowing how to model and present that data. Depending on the problem at hand, it might also include a working knowledge of specific data analysis tools and procedures.

Having a solid grasp of data analysis techniques is useful if you’re leading a problem solving workshop but if you’re not an expert, don’t worry. Bring people into the group who has this skill set and help your team be more effective as a result.

Decision making

All problems need a solution and all solutions require that someone make the decision to implement them. Without strong decision making skills, teams can become bogged down in discussion and less effective as a result.

Making decisions is a key part of the problem solving process. It’s important to remember that decision making is not restricted to the leadership team. Every staff member makes decisions every day and developing these skills ensures that your team is able to solve problems at any scale. Remember that making decisions does not mean leaping to the first solution but weighing up the options and coming to an informed, well thought out solution to any given problem that works for the whole team.

Lightning Decision Jam (LDJ) #action #decision making #problem solving #issue analysis #innovation #design #remote-friendly The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

Dependability

Most complex organizational problems require multiple people to be involved in delivering the solution. Ensuring that the team and organization can depend on you to take the necessary actions and communicate where necessary is key to ensuring problems are solved effectively.

Being dependable also means working to deadlines and to brief. It is often a matter of creating trust in a team so that everyone can depend on one another to complete the agreed actions in the agreed time frame so that the team can move forward together. Being undependable can create problems of friction and can limit the effectiveness of your solutions so be sure to bear this in mind throughout a project.

Team Purpose & Culture #team #hyperisland #culture #remote-friendly This is an essential process designed to help teams define their purpose (why they exist) and their culture (how they work together to achieve that purpose). Defining these two things will help any team to be more focused and aligned. With support of tangible examples from other companies, the team members work as individuals and a group to codify the way they work together. The goal is a visual manifestation of both the purpose and culture that can be put up in the team’s work space.

Emotional intelligence

Emotional intelligence is an important skill for any successful team member, whether communicating internally or with clients or users. In the problem solving process, emotional intelligence means being attuned to how people are feeling and thinking, communicating effectively and being self-aware of what you bring to a room.

There are often differences of opinion when working through problem solving processes, and it can be easy to let things become impassioned or combative. Developing your emotional intelligence means being empathetic to your colleagues and managing your own emotions throughout the problem and solution process. Be kind, be thoughtful and put your points across care and attention.

Being emotionally intelligent is a skill for life and by deploying it at work, you can not only work efficiently but empathetically. Check out the emotional culture workshop template for more!

Facilitation

As we’ve clarified in our facilitation skills post, facilitation is the art of leading people through processes towards agreed-upon objectives in a manner that encourages participation, ownership, and creativity by all those involved. While facilitation is a set of interrelated skills in itself, the broad definition of facilitation can be invaluable when it comes to problem solving. Leading a team through a problem solving process is made more effective if you improve and utilize facilitation skills – whether you’re a manager, team leader or external stakeholder.

The Six Thinking Hats #creative thinking #meeting facilitation #problem solving #issue resolution #idea generation #conflict resolution The Six Thinking Hats are used by individuals and groups to separate out conflicting styles of thinking. They enable and encourage a group of people to think constructively together in exploring and implementing change, rather than using argument to fight over who is right and who is wrong.

Flexibility

Being flexible is a vital skill when it comes to problem solving. This does not mean immediately bowing to pressure or changing your opinion quickly: instead, being flexible is all about seeing things from new perspectives, receiving new information and factoring it into your thought process.

Flexibility is also important when it comes to rolling out solutions. It might be that other organizational projects have greater priority or require the same resources as your chosen solution. Being flexible means understanding needs and challenges across the team and being open to shifting or arranging your own schedule as necessary. Again, this does not mean immediately making way for other projects. It’s about articulating your own needs, understanding the needs of others and being able to come to a meaningful compromise.

The Creativity Dice #creativity #problem solving #thiagi #issue analysis Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

Working in any group can lead to unconscious elements of groupthink or situations in which you may not wish to be entirely honest. Disagreeing with the opinions of the executive team or wishing to save the feelings of a coworker can be tricky to navigate, but being honest is absolutely vital when to comes to developing effective solutions and ensuring your voice is heard.

Remember that being honest does not mean being brutally candid. You can deliver your honest feedback and opinions thoughtfully and without creating friction by using other skills such as emotional intelligence.

Explore your Values #hyperisland #skills #values #remote-friendly Your Values is an exercise for participants to explore what their most important values are. It’s done in an intuitive and rapid way to encourage participants to follow their intuitive feeling rather than over-thinking and finding the “correct” values. It is a good exercise to use to initiate reflection and dialogue around personal values.

Initiative

The problem solving process is multi-faceted and requires different approaches at certain points of the process. Taking initiative to bring problems to the attention of the team, collect data or lead the solution creating process is always valuable. You might even roadtest your own small scale solutions or brainstorm before a session. Taking initiative is particularly effective if you have good deal of knowledge in that area or have ownership of a particular project and want to get things kickstarted.

That said, be sure to remember to honor the process and work in service of the team. If you are asked to own one part of the problem solving process and you don’t complete that task because your initiative leads you to work on something else, that’s not an effective method of solving business challenges.

15% Solutions #action #liberating structures #remote-friendly You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference. 15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change. With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

Impartiality

A particularly useful problem solving skill for product owners or managers is the ability to remain impartial throughout much of the process. In practice, this means treating all points of view and ideas brought forward in a meeting equally and ensuring that your own areas of interest or ownership are not favored over others.

There may be a stage in the process where a decision maker has to weigh the cost and ROI of possible solutions against the company roadmap though even then, ensuring that the decision made is based on merit and not personal opinion.

Empathy map #frame insights #create #design #issue analysis An empathy map is a tool to help a design team to empathize with the people they are designing for. You can make an empathy map for a group of people or for a persona. To be used after doing personas when more insights are needed.

Being a good leader means getting a team aligned, energized and focused around a common goal. In the problem solving process, strong leadership helps ensure that the process is efficient, that any conflicts are resolved and that a team is managed in the direction of success.

It’s common for managers or executives to assume this role in a problem solving workshop, though it’s important that the leader maintains impartiality and does not bulldoze the group in a particular direction. Remember that good leadership means working in service of the purpose and team and ensuring the workshop is a safe space for employees of any level to contribute. Take a look at our leadership games and activities post for more exercises and methods to help improve leadership in your organization.

Leadership Pizza #leadership #team #remote-friendly This leadership development activity offers a self-assessment framework for people to first identify what skills, attributes and attitudes they find important for effective leadership, and then assess their own development and initiate goal setting.

In the context of problem solving, mediation is important in keeping a team engaged, happy and free of conflict. When leading or facilitating a problem solving workshop, you are likely to run into differences of opinion. Depending on the nature of the problem, certain issues may be brought up that are emotive in nature.

Being an effective mediator means helping those people on either side of such a divide are heard, listen to one another and encouraged to find common ground and a resolution. Mediating skills are useful for leaders and managers in many situations and the problem solving process is no different.

Conflict Responses #hyperisland #team #issue resolution A workshop for a team to reflect on past conflicts, and use them to generate guidelines for effective conflict handling. The workshop uses the Thomas-Killman model of conflict responses to frame a reflective discussion. Use it to open up a discussion around conflict with a team.

Planning

Solving organizational problems is much more effective when following a process or problem solving model. Planning skills are vital in order to structure, deliver and follow-through on a problem solving workshop and ensure your solutions are intelligently deployed.

Planning skills include the ability to organize tasks and a team, plan and design the process and take into account any potential challenges. Taking the time to plan carefully can save time and frustration later in the process and is valuable for ensuring a team is positioned for success.

3 Action Steps #hyperisland #action #remote-friendly This is a small-scale strategic planning session that helps groups and individuals to take action toward a desired change. It is often used at the end of a workshop or programme. The group discusses and agrees on a vision, then creates some action steps that will lead them towards that vision. The scope of the challenge is also defined, through discussion of the helpful and harmful factors influencing the group.

Prioritization

As organisations grow, the scale and variation of problems they face multiplies. Your team or is likely to face numerous challenges in different areas and so having the skills to analyze and prioritize becomes very important, particularly for those in leadership roles.

A thorough problem solving process is likely to deliver multiple solutions and you may have several different problems you wish to solve simultaneously. Prioritization is the ability to measure the importance, value, and effectiveness of those possible solutions and choose which to enact and in what order. The process of prioritization is integral in ensuring the biggest challenges are addressed with the most impactful solutions.

Impact and Effort Matrix #gamestorming #decision making #action #remote-friendly In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

Project management

Some problem solving skills are utilized in a workshop or ideation phases, while others come in useful when it comes to decision making. Overseeing an entire problem solving process and ensuring its success requires strong project management skills.

While project management incorporates many of the other skills listed here, it is important to note the distinction of considering all of the factors of a project and managing them successfully. Being able to negotiate with stakeholders, manage tasks, time and people, consider costs and ROI, and tie everything together is massively helpful when going through the problem solving process.

Record keeping

Working out meaningful solutions to organizational challenges is only one part of the process. Thoughtfully documenting and keeping records of each problem solving step for future consultation is important in ensuring efficiency and meaningful change.

For example, some problems may be lower priority than others but can be revisited in the future. If the team has ideated on solutions and found some are not up to the task, record those so you can rule them out and avoiding repeating work. Keeping records of the process also helps you improve and refine your problem solving model next time around!

Personal Kanban #gamestorming #action #agile #project planning Personal Kanban is a tool for organizing your work to be more efficient and productive. It is based on agile methods and principles.

Research skills

Conducting research to support both the identification of problems and the development of appropriate solutions is important for an effective process. Knowing where to go to collect research, how to conduct research efficiently, and identifying pieces of research are relevant are all things a good researcher can do well.

In larger groups, not everyone has to demonstrate this ability in order for a problem solving workshop to be effective. That said, having people with research skills involved in the process, particularly if they have existing area knowledge, can help ensure the solutions that are developed with data that supports their intention. Remember that being able to deliver the results of research efficiently and in a way the team can easily understand is also important. The best data in the world is only as effective as how it is delivered and interpreted.

Customer experience map #ideation #concepts #research #design #issue analysis #remote-friendly Customer experience mapping is a method of documenting and visualizing the experience a customer has as they use the product or service. It also maps out their responses to their experiences. To be used when there is a solution (even in a conceptual stage) that can be analyzed.

Risk management

Managing risk is an often overlooked part of the problem solving process. Solutions are often developed with the intention of reducing exposure to risk or solving issues that create risk but sometimes, great solutions are more experimental in nature and as such, deploying them needs to be carefully considered.

Managing risk means acknowledging that there may be risks associated with more out of the box solutions or trying new things, but that this must be measured against the possible benefits and other organizational factors.

Be informed, get the right data and stakeholders in the room and you can appropriately factor risk into your decision making process.

Decisions, Decisions… #communication #decision making #thiagi #action #issue analysis When it comes to decision-making, why are some of us more prone to take risks while others are risk-averse? One explanation might be the way the decision and options were presented. This exercise, based on Kahneman and Tversky’s classic study , illustrates how the framing effect influences our judgement and our ability to make decisions . The participants are divided into two groups. Both groups are presented with the same problem and two alternative programs for solving them. The two programs both have the same consequences but are presented differently. The debriefing discussion examines how the framing of the program impacted the participant’s decision.

Team-building

No single person is as good at problem solving as a team. Building an effective team and helping them come together around a common purpose is one of the most important problem solving skills, doubly so for leaders. By bringing a team together and helping them work efficiently, you pave the way for team ownership of a problem and the development of effective solutions.

In a problem solving workshop, it can be tempting to jump right into the deep end, though taking the time to break the ice, energize the team and align them with a game or exercise will pay off over the course of the day.

Remember that you will likely go through the problem solving process multiple times over an organization’s lifespan and building a strong team culture will make future problem solving more effective. It’s also great to work with people you know, trust and have fun with. Working on team building in and out of the problem solving process is a hallmark of successful teams that can work together to solve business problems.

9 Dimensions Team Building Activity #ice breaker #teambuilding #team #remote-friendly 9 Dimensions is a powerful activity designed to build relationships and trust among team members. There are 2 variations of this icebreaker. The first version is for teams who want to get to know each other better. The second version is for teams who want to explore how they are working together as a team.

Time management

The problem solving process is designed to lead a team from identifying a problem through to delivering a solution and evaluating its effectiveness. Without effective time management skills or timeboxing of tasks, it can be easy for a team to get bogged down or be inefficient.

By using a problem solving model and carefully designing your workshop, you can allocate time efficiently and trust that the process will deliver the results you need in a good timeframe.

Time management also comes into play when it comes to rolling out solutions, particularly those that are experimental in nature. Having a clear timeframe for implementing and evaluating solutions is vital for ensuring their success and being able to pivot if necessary.

Improving your skills at problem solving is often a career-long pursuit though there are methods you can use to make the learning process more efficient and to supercharge your problem solving skillset.

Remember that the skills you need to be a great problem solver have a large overlap with those skills you need to be effective in any role. Investing time and effort to develop your active listening or critical thinking skills is valuable in any context. Here are 7 ways to improve your problem solving skills.

Share best practices

Remember that your team is an excellent source of skills, wisdom, and techniques and that you should all take advantage of one another where possible. Best practices that one team has for solving problems, conducting research or making decisions should be shared across the organization. If you have in-house staff that have done active listening training or are data analysis pros, have them lead a training session.

Your team is one of your best resources. Create space and internal processes for the sharing of skills so that you can all grow together.

Ask for help and attend training

Once you’ve figured out you have a skills gap, the next step is to take action to fill that skills gap. That might be by asking your superior for training or coaching, or liaising with team members with that skill set. You might even attend specialized training for certain skills – active listening or critical thinking, for example, are business-critical skills that are regularly offered as part of a training scheme.

Whatever method you choose, remember that taking action of some description is necessary for growth. Whether that means practicing, getting help, attending training or doing some background reading, taking active steps to improve your skills is the way to go.

Learn a process

Problem solving can be complicated, particularly when attempting to solve large problems for the first time. Using a problem solving process helps give structure to your problem solving efforts and focus on creating outcomes, rather than worrying about the format.

Tools such as the seven-step problem solving process above are effective because not only do they feature steps that will help a team solve problems, they also develop skills along the way. Each step asks for people to engage with the process using different skills and in doing so, helps the team learn and grow together. Group processes of varying complexity and purpose can also be found in the SessionLab library of facilitation techniques . Using a tried and tested process and really help ease the learning curve for both those leading such a process, as well as those undergoing the purpose.

Effective teams make decisions about where they should and shouldn’t expend additional effort. By using a problem solving process, you can focus on the things that matter, rather than stumbling towards a solution haphazardly.

Create a feedback loop

Some skills gaps are more obvious than others. It’s possible that your perception of your active listening skills differs from those of your colleagues.

It’s valuable to create a system where team members can provide feedback in an ordered and friendly manner so they can all learn from one another. Only by identifying areas of improvement can you then work to improve them.

Remember that feedback systems require oversight and consideration so that they don’t turn into a place to complain about colleagues. Design the system intelligently so that you encourage the creation of learning opportunities, rather than encouraging people to list their pet peeves.

While practice might not make perfect, it does make the problem solving process easier. If you are having trouble with critical thinking, don’t shy away from doing it. Get involved where you can and stretch those muscles as regularly as possible.

Problem solving skills come more naturally to some than to others and that’s okay. Take opportunities to get involved and see where you can practice your skills in situations outside of a workshop context. Try collaborating in other circumstances at work or conduct data analysis on your own projects. You can often develop those skills you need for problem solving simply by doing them. Get involved!

Use expert exercises and methods

Learn from the best. Our library of 700+ facilitation techniques is full of activities and methods that help develop the skills you need to be an effective problem solver. Check out our templates to see how to approach problem solving and other organizational challenges in a structured and intelligent manner.

There is no single approach to improving problem solving skills, but by using the techniques employed by others you can learn from their example and develop processes that have seen proven results.

Try new ways of thinking and change your mindset

Using tried and tested exercises that you know well can help deliver results, but you do run the risk of missing out on the learning opportunities offered by new approaches. As with the problem solving process, changing your mindset can remove blockages and be used to develop your problem solving skills.

Most teams have members with mixed skill sets and specialties. Mix people from different teams and share skills and different points of view. Teach your customer support team how to use design thinking methods or help your developers with conflict resolution techniques. Try switching perspectives with facilitation techniques like Flip It! or by using new problem solving methodologies or models. Give design thinking, liberating structures or lego serious play a try if you want to try a new approach. You will find that framing problems in new ways and using existing skills in new contexts can be hugely useful for personal development and improving your skillset. It’s also a lot of fun to try new things. Give it a go!

Encountering business challenges and needing to find appropriate solutions is not unique to your organization. Lots of very smart people have developed methods, theories and approaches to help develop problem solving skills and create effective solutions. Learn from them!

Books like The Art of Thinking Clearly , Think Smarter, or Thinking Fast, Thinking Slow are great places to start, though it’s also worth looking at blogs related to organizations facing similar problems to yours, or browsing for success stories. Seeing how Dropbox massively increased growth and working backward can help you see the skills or approach you might be lacking to solve that same problem. Learning from others by reading their stories or approaches can be time-consuming but ultimately rewarding.

A tired, distracted mind is not in the best position to learn new skills. It can be tempted to burn the candle at both ends and develop problem solving skills outside of work. Absolutely use your time effectively and take opportunities for self-improvement, though remember that rest is hugely important and that without letting your brain rest, you cannot be at your most effective.

Creating distance between yourself and the problem you might be facing can also be useful. By letting an idea sit, you can find that a better one presents itself or you can develop it further. Take regular breaks when working and create a space for downtime. Remember that working smarter is preferable to working harder and that self-care is important for any effective learning or improvement process.

Want to design better group processes?

Over to you

Now we’ve explored some of the key problem solving skills and the problem solving steps necessary for an effective process, you’re ready to begin developing more effective solutions and leading problem solving workshops.

Need more inspiration? Check out our post on problem solving activities you can use when guiding a group towards a great solution in your next workshop or meeting. Have questions? Did you have a great problem solving technique you use with your team? Get in touch in the comments below. We’d love to chat!

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Introduction to Problem Solving Skills

What is problem solving and why is it important.

The ability to solve problems is a basic life skill and is essential to our day-to-day lives, at home, at school, and at work. We solve problems every day without really thinking about how we solve them. For example: it’s raining and you need to go to the store. What do you do? There are lots of possible solutions. Take your umbrella and walk. If you don't want to get wet, you can drive, or take the bus. You might decide to call a friend for a ride, or you might decide to go to the store another day. There is no right way to solve this problem and different people will solve it differently.

Problem solving is the process of identifying a problem, developing possible solution paths, and taking the appropriate course of action.

Why is problem solving important? Good problem solving skills empower you not only in your personal life but are critical in your professional life. In the current fast-changing global economy, employers often identify everyday problem solving as crucial to the success of their organizations. For employees, problem solving can be used to develop practical and creative solutions, and to show independence and initiative to employers.

Throughout this case study you will be asked to jot down your thoughts in idea logs. These idea logs are used for reflection on concepts and for answering short questions. When you click on the "Next" button, your responses will be saved for that page. If you happen to close the webpage, you will lose your work on the page you were on, but previous pages will be saved. At the end of the case study, click on the "Finish and Export to PDF" button to acknowledge completion of the case study and receive a PDF document of your idea logs.

What Does Problem Solving Look Like?

IDEAL heuristic strategy for problem solving

The ability to solve problems is a skill, and just like any other skill, the more you practice, the better you get. So how exactly do you practice problem solving? Learning about different problem solving strategies and when to use them will give you a good start. Problem solving is a process. Most strategies provide steps that help you identify the problem and choose the best solution. There are two basic types of strategies: algorithmic and heuristic.

Algorithmic strategies are traditional step-by-step guides to solving problems. They are great for solving math problems (in algebra: multiply and divide, then add or subtract) or for helping us remember the correct order of things (a mnemonic such as “Spring Forward, Fall Back” to remember which way the clock changes for daylight saving time, or “Righty Tighty, Lefty Loosey” to remember what direction to turn bolts and screws). Algorithms are best when there is a single path to the correct solution.

But what do you do when there is no single solution for your problem? Heuristic methods are general guides used to identify possible solutions. A popular one that is easy to remember is IDEAL [ Bransford & Stein, 1993 ] :

I dentify the problem
D efine the context of the problem
E xplore possible strategies
A ct on best solution

IDEAL is just one problem solving strategy. Building a toolbox of problem solving strategies will improve your problem solving skills. With practice, you will be able to recognize and use multiple strategies to solve complex problems.

Watch the video

What is the best way to get a peanut out of a tube that cannot be moved? Watch a chimpanzee solve this problem in the video below [ Geert Stienissen, 2010 ].

[PDF transcript]

Describe the series of steps you think the chimpanzee used to solve this problem.

[Page 2: What does Problem Solving Look Like?] Describe the series of steps you think the chimpanzee used to solve this problem.

Think of an everyday problem you've encountered recently and describe your steps for solving it.

[Page 2: What does Problem Solving Look Like?] Think of an everyday problem you've encountered recently and describe your steps for solving it.

Developing Problem Solving Processes

Problem solving is a process that uses steps to solve problems. But what does that really mean? Let's break it down and start building our toolbox of problem solving strategies.

What is the first step of solving any problem? The first step is to recognize that there is a problem and identify the right cause of the problem. This may sound obvious, but similar problems can arise from different events, and the real issue may not always be apparent. To really solve the problem, it's important to find out what started it all. This is called identifying the root cause .

Example: You and your classmates have been working long hours on a project in the school's workshop. The next afternoon, you try to use your student ID card to access the workshop, but discover that your magnetic strip has been demagnetized. Since the card was a couple of years old, you chalk it up to wear and tear and get a new ID card. Later that same week you learn that several of your classmates had the same problem! After a little investigation, you discover that a strong magnet was stored underneath a workbench in the workshop. The magnet was the root cause of the demagnetized student ID cards.

The best way to identify the root cause of the problem is to ask questions and gather information. If you have a vague problem, investigating facts is more productive than guessing a solution. Ask yourself questions about the problem. What do you know about the problem? What do you not know? When was the last time it worked correctly? What has changed since then? Can you diagram the process into separate steps? Where in the process is the problem occurring? Be curious, ask questions, gather facts, and make logical deductions rather than assumptions.

Watch Adam Savage from Mythbusters, describe his problem solving process [ ForaTv, 2010 ]. As you watch this section of the video, try to identify the questions he asks and the different strategies he uses.

Adam Savage shared many of his problem solving processes. List the ones you think are the five most important. Your list may be different from other people in your class—that's ok!

[Page 3: Developing Problem Solving Processes] Adam Savage shared many of his problem solving processes. List the ones you think are the five most important.

“The ability to ask the right question is more than half the battle of finding the answer.” — Thomas J. Watson , founder of IBM

Voices From the Field: Solving Problems

In manufacturing facilities and machine shops, everyone on the floor is expected to know how to identify problems and find solutions. Today's employers look for the following skills in new employees: to analyze a problem logically, formulate a solution, and effectively communicate with others.

In this video, industry professionals share their own problem solving processes, the problem solving expectations of their employees, and an example of how a problem was solved.

Meet the Partners:

Taconic High School in Pittsfield, Massachusetts, is a comprehensive, fully accredited high school with special programs in Health Technology, Manufacturing Technology, and Work-Based Learning.
Berkshire Community College in Pittsfield, Massachusetts, prepares its students with applied manufacturing technical skills, providing hands-on experience at industrial laboratories and manufacturing facilities, and instructing them in current technologies.
H.C. Starck in Newton, Massachusetts, specializes in processing and manufacturing technology metals, such as tungsten, niobium, and tantalum. In almost 100 years of experience, they hold over 900 patents, and continue to innovate and develop new products.
Nypro Healthcare in Devens, Massachusetts, specializes in precision injection-molded healthcare products. They are committed to good manufacturing processes including lean manufacturing and process validation.

Making Decisions

Now that you have a couple problem solving strategies in your toolbox, let's practice. In this exercise, you are given a scenario and you will be asked to decide what steps you would take to identify and solve the problem.

Scenario: You are a new employee and have just finished your training. As your first project, you have been assigned the milling of several additional components for a regular customer. Together, you and your trainer, Bill, set up for the first run. Checking your paperwork, you gather the tools and materials on the list. As you are mounting the materials on the table, you notice that you didn't grab everything and hurriedly grab a few more items from one of the bins. Once the material is secured on the CNC table, you load tools into the tool carousel in the order listed on the tool list and set the fixture offsets.

Bill tells you that since this is a rerun of a job several weeks ago, the CAD/CAM model has already been converted to CNC G-code. Bill helps you download the code to the CNC machine. He gives you the go-ahead and leaves to check on another employee. You decide to start your first run.

What problems did you observe in the video?

[Page 5: Making Decisions] What problems did you observe in the video?
What do you do next?
Try to fix it yourself.
Ask your trainer for help.

As you are cleaning up, you think about what happened and wonder why it happened. You try to create a mental picture of what happened. You are not exactly sure what the end mill hit, but it looked like it might have hit the dowel pin. You wonder if you grabbed the correct dowel pins from the bins earlier.

You can think of two possible next steps. You can recheck the dowel pin length to make sure it is the correct length, or do a dry run using the CNC single step or single block function with the spindle empty to determine what actually happened.

Check the dowel pins.
Use the single step/single block function to determine what happened.

You notice that your trainer, Bill, is still on the floor and decide to ask him for help. You describe the problem to him. Bill asks if you know what the end mill ran into. You explain that you are not sure but you think it was the dowel pin. Bill reminds you that it is important to understand what happened so you can fix the correct problem. He suggests that you start all over again and begin with a dry run using the single step/single block function, with the spindle empty, to determine what it hit. Or, since it happened at the end, he mentions that you can also check the G-code to make sure the Z-axis is raised before returning to the home position.

Run the single step/single block function.
Edit the G-code to raise the Z-axis.

You finish cleaning up and check the CNC for any damage. Luckily, everything looks good. You check your paperwork and gather the components and materials again. You look at the dowel pins you used earlier, and discover that they are not the right length. As you go to grab the correct dowel pins, you have to search though several bins. For the first time, you are aware of the mess - it looks like the dowel pins and other items have not been put into the correctly labeled bins. You spend 30 minutes straightening up the bins and looking for the correct dowel pins.

Finally finding them, you finish setting up. You load tools into the tool carousel in the order listed on the tool list and set the fixture offsets. Just to make sure, you use the CNC single step/single block function, to do a dry run of the part. Everything looks good! You are ready to create your first part. The first component is done, and, as you admire your success, you notice that the part feels hotter than it should.

You wonder why? You go over the steps of the process to mentally figure out what could be causing the residual heat. You wonder if there is a problem with the CNC's coolant system or if the problem is in the G-code.

Look at the G-code.

After thinking about the problem, you decide that maybe there's something wrong with the setup. First, you clean up the damaged materials and remove the broken tool. You check the CNC machine carefully for any damage. Luckily, everything looks good. It is time to start over again from the beginning.

You again check your paperwork and gather the tools and materials on the setup sheet. After securing the new materials, you use the CNC single step/single block function with the spindle empty, to do a dry run of the part. You watch carefully to see if you can figure out what happened. It looks to you like the spindle barely misses hitting the dowel pin. You determine that the end mill was broken when it hit the dowel pin while returning to the start position.

After conducting a dry run using the single step/single block function, you determine that the end mill was damaged when it hit the dowel pin on its return to the home position. You discuss your options with Bill. Together, you decide the best thing to do would be to edit the G-code and raise the Z-axis before returning to home. You open the CNC control program and edit the G-code. Just to make sure, you use the CNC single step/single block function, to do another dry run of the part. You are ready to create your first part. It works. You first part is completed. Only four more to go.

As you are cleaning up, you notice that the components are hotter than you expect and the end mill looks more worn than it should be. It dawns on you that while you were milling the component, the coolant didn't turn on. You wonder if it is a software problem in the G-code or hardware problem with the CNC machine.

It's the end of the day and you decide to finish the rest of the components in the morning.

You decide to look at the G-code in the morning.
You leave a note on the machine, just in case.

You decide that the best thing to do would be to edit the G-code and raise the Z-axis of the spindle before it returns to home. You open the CNC control program and edit the G-code.

While editing the G-code to raise the Z-axis, you notice that the coolant is turned off at the beginning of the code and at the end of the code. The coolant command error caught your attention because your coworker, Mark, mentioned having a similar issue during lunch. You change the coolant command to turn the mist on.

You decide to talk with your supervisor.
You discuss what happened with a coworker over lunch.

As you reflect on the residual heat problem, you think about the machining process and the factors that could have caused the issue. You try to think of anything and everything that could be causing the issue. Are you using the correct tool for the specified material? Are you using the specified material? Is it running at the correct speed? Is there enough coolant? Are there chips getting in the way?

Wait, was the coolant turned on? As you replay what happened in your mind, you wonder why the coolant wasn't turned on. You decide to look at the G-code to find out what is going on.

From the milling machine computer, you open the CNC G-code. You notice that there are no coolant commands. You add them in and on the next run, the coolant mist turns on and the residual heat issues is gone. Now, its on to creating the rest of the parts.

Have you ever used brainstorming to solve a problem? Chances are, you've probably have, even if you didn't realize it.

You notice that your trainer, Bill, is on the floor and decide to ask him for help. You describe the problem with the end mill breaking, and how you discovered that items are not being returned to the correctly labeled bins. You think this caused you to grab the incorrect length dowel pins on your first run. You have sorted the bins and hope that the mess problem is fixed. You then go on to tell Bill about the residual heat issue with the completed part.

Together, you go to the milling machine. Bill shows you how to check the oil and coolant levels. Everything looks good at the machine level. Next, on the CNC computer, you open the CNC G-code. While looking at the code, Bill points out that there are no coolant commands. Bill adds them in and when you rerun the program, it works.

Bill is glad you mentioned the problem to him. You are the third worker to mention G-code issues over the last week. You noticed the coolant problems in your G-code, John noticed a Z-axis issue in his G-code, and Sam had issues with both the Z-axis and the coolant. Chances are, there is a bigger problem and Bill will need to investigate the root cause .

Talking with Bill, you discuss the best way to fix the problem. Bill suggests editing the G-code to raise the Z-axis of the spindle before it returns to its home position. You open the CNC control program and edit the G-code. Following the setup sheet, you re-setup the job and use the CNC single step/single block function, to do another dry run of the part. Everything looks good, so you run the job again and create the first part. It works. Since you need four of each component, you move on to creating the rest of them before cleaning up and leaving for the day.

It's a new day and you have new components to create. As you are setting up, you go in search of some short dowel pins. You discover that the bins are a mess and components have not been put away in the correctly labeled bins. You wonder if this was the cause of yesterday's problem. As you reorganize the bins and straighten up the mess, you decide to mention the mess issue to Bill in your afternoon meeting.

You describe the bin mess and using the incorrect length dowels to Bill. He is glad you mentioned the problem to him. You are not the first person to mention similar issues with tools and parts not being put away correctly. Chances are there is a bigger safety issue here that needs to be addressed in the next staff meeting.

In any workplace, following proper safety and cleanup procedures is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly and sometimes dangerous equipment. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money.

You now know that the end mill was damaged when it hit the dowel pin. It seems to you that the easiest thing to do would be to edit the G-code and raise the Z-axis position of the spindle before it returns to the home position. You open the CNC control program and edit the G-code, raising the Z-axis. Starting over, you follow the setup sheet and re-setup the job. This time, you use the CNC single step/single block function, to do another dry run of the part. Everything looks good, so you run the job again and create the first part.

At the end of the day, you are reviewing your progress with your trainer, Bill. After you describe the day's events, he reminds you to always think about safety and the importance of following work procedures. He decides to bring the issue up in the next morning meeting as a reminder to everyone.

In any workplace, following proper procedures (especially those that involve safety) is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money. One tool to improve communication is the morning meeting or huddle.

The next morning, you check the G-code to determine what is wrong with the coolant. You notice that the coolant is turned off at the beginning of the code and also at the end of the code. This is strange. You change the G-code to turn the coolant on at the beginning of the run and off at the end. This works and you create the rest of the parts.

Throughout the day, you keep wondering what caused the G-code error. At lunch, you mention the G-code error to your coworker, John. John is not surprised. He said that he encountered a similar problem earlier this week. You decide to talk with your supervisor the next time you see him.

You are in luck. You see your supervisor by the door getting ready to leave. You hurry over to talk with him. You start off by telling him about how you asked Bill for help. Then you tell him there was a problem and the end mill was damaged. You describe the coolant problem in the G-code. Oh, and by the way, John has seen a similar problem before.

Your supervisor doesn't seem overly concerned, errors happen. He tells you "Good job, I am glad you were able to fix the issue." You are not sure whether your supervisor understood your explanation of what happened or that it had happened before.

The challenge of communicating in the workplace is learning how to share your ideas and concerns. If you need to tell your supervisor that something is not going well, it is important to remember that timing, preparation, and attitude are extremely important.

It is the end of your shift, but you want to let the next shift know that the coolant didn't turn on. You do not see your trainer or supervisor around. You decide to leave a note for the next shift so they are aware of the possible coolant problem. You write a sticky note and leave it on the monitor of the CNC control system.

How effective do you think this solution was? Did it address the problem?

In this scenario, you discovered several problems with the G-code that need to be addressed. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring and avoid injury to personnel. The challenge of communicating in the workplace is learning how and when to share your ideas and concerns. If you need to tell your co-workers or supervisor that there is a problem, it is important to remember that timing and the method of communication are extremely important.

You are able to fix the coolant problem in the G-code. While you are glad that the problem is fixed, you are worried about why it happened in the first place. It is important to remember that if a problem keeps reappearing, you may not be fixing the right problem. You may only be addressing the symptoms.

You decide to talk to your trainer. Bill is glad you mentioned the problem to him. You are the third worker to mention G-code issues over the last week. You noticed the coolant problems in your G-code, John noticed a Z-axis issue in his G-code, and Sam had issues with both the Z-axis and the coolant. Chances are, there is a bigger problem and Bill will need to investigate the root cause .

Over lunch, you ask your coworkers about the G-code problem and what may be causing the error. Several people mention having similar problems but do not know the cause.

You have now talked to three coworkers who have all experienced similar coolant G-code problems. You make a list of who had the problem, when they had the problem, and what each person told you.

When you see your supervisor later that afternoon, you are ready to talk with him. You describe the problem you had with your component and the damaged bit. You then go on to tell him about talking with Bill and discovering the G-code issue. You show him your notes on your coworkers' coolant issues, and explain that you think there might be a bigger problem.

You supervisor thanks you for your initiative in identifying this problem. It sounds like there is a bigger problem and he will need to investigate the root cause. He decides to call a team huddle to discuss the issue, gather more information, and talk with the team about the importance of communication.

Root Cause Analysis

Root cause analysis ( RCA ) is a method of problem solving that identifies the underlying causes of an issue. Root cause analysis helps people answer the question of why the problem occurred in the first place. RCA uses clear cut steps in its associated tools, like the "5 Whys Analysis" and the "Cause and Effect Diagram," to identify the origin of the problem, so that you can:

Determine what happened.
Determine why it happened.
Fix the problem so it won’t happen again.

RCA works under the idea that systems and events are connected. An action in one area triggers an action in another, and another, and so on. By tracing back these actions, you can discover where the problem started and how it developed into the problem you're now facing. Root cause analysis can prevent problems from recurring, reduce injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money. There are many different RCA techniques available to determine the root cause of a problem. These are just a few:

Root Cause Analysis Tools
5 Whys Analysis
Fishbone or Cause and Effect Diagram
Pareto Analysis

How Huddles Work

Communication is a vital part of any setting where people work together. Effective communication helps employees and managers form efficient teams. It builds trusts between employees and management, and reduces unnecessary competition because each employee knows how their part fits in the larger goal.

One tool that management can use to promote communication in the workplace is the huddle . Just like football players on the field, a huddle is a short meeting where everyone is standing in a circle. A daily team huddle ensures that team members are aware of changes to the schedule, reiterated problems and safety issues, and how their work impacts one another. When done right, huddles create collaboration, communication, and accountability to results. Impromptu huddles can be used to gather information on a specific issue and get each team member's input.

The most important thing to remember about huddles is that they are short, lasting no more than 10 minutes, and their purpose is to communicate and identify. In essence, a huddle’s purpose is to identify priorities, communicate essential information, and discover roadblocks to productivity.

Who uses huddles? Many industries and companies use daily huddles. At first thought, most people probably think of hospitals and their daily patient update meetings, but lots of managers use daily meetings to engage their employees. Here are a few examples:

Brian Scudamore, CEO of 1-800-Got-Junk? , uses the daily huddle as an operational tool to take the pulse of his employees and as a motivational tool. Watch a morning huddle meeting .
Fusion OEM, an outsourced manufacturing and production company. What do employees take away from the daily huddle meeting .
Biz-Group, a performance consulting group. Tips for a successful huddle .

Brainstorming

One tool that can be useful in problem solving is brainstorming . Brainstorming is a creativity technique designed to generate a large number of ideas for the solution to a problem. The method was first popularized in 1953 by Alex Faickney Osborn in the book Applied Imagination . The goal is to come up with as many ideas as you can in a fixed amount of time. Although brainstorming is best done in a group, it can be done individually. Like most problem solving techniques, brainstorming is a process.

Define a clear objective.
Have an agreed a time limit.
During the brainstorming session, write down everything that comes to mind, even if the idea sounds crazy.
If one idea leads to another, write down that idea too.
Combine and refine ideas into categories of solutions.
Assess and analyze each idea as a potential solution.

When used during problem solving, brainstorming can offer companies new ways of encouraging staff to think creatively and improve production. Brainstorming relies on team members' diverse experiences, adding to the richness of ideas explored. This means that you often find better solutions to the problems. Team members often welcome the opportunity to contribute ideas and can provide buy-in for the solution chosen—after all, they are more likely to be committed to an approach if they were involved in its development. What's more, because brainstorming is fun, it helps team members bond.

Watch Peggy Morgan Collins, a marketing executive at Power Curve Communications discuss How to Stimulate Effective Brainstorming .
Watch Kim Obbink, CEO of Filter Digital, a digital content company, and her team share their top five rules for How to Effectively Generate Ideas .

Importance of Good Communication and Problem Description

talking too much when describing a problem

Communication is one of the most frequent activities we engage in on a day-to-day basis. At some point, we have all felt that we did not effectively communicate an idea as we would have liked. The key to effective communication is preparation. Rather than attempting to haphazardly improvise something, take a few minutes and think about what you want say and how you will say it. If necessary, write yourself a note with the key points or ideas in the order you want to discuss them. The notes can act as a reminder or guide when you talk to your supervisor.

Tips for clear communication of an issue:

Provide a clear summary of your problem. Start at the beginning, give relevant facts, timelines, and examples.
Avoid including your opinion or personal attacks in your explanation.
Avoid using words like "always" or "never," which can give the impression that you are exaggerating the problem.
If this is an ongoing problem and you have collected documentation, give it to your supervisor once you have finished describing the problem.
Remember to listen to what's said in return; communication is a two-way process.

Not all communication is spoken. Body language is nonverbal communication that includes your posture, your hands and whether you make eye contact. These gestures can be subtle or overt, but most importantly they communicate meaning beyond what is said. When having a conversation, pay attention to how you stand. A stiff position with arms crossed over your chest may imply that you are being defensive even if your words state otherwise. Shoving your hands in your pockets when speaking could imply that you have something to hide. Be wary of using too many hand gestures because this could distract listeners from your message.

The challenge of communicating in the workplace is learning how and when to share your ideas or concerns. If you need to tell your supervisor or co-worker about something that is not going well, keep in mind that good timing and good attitude will go a long way toward helping your case.

Like all skills, effective communication needs to be practiced. Toastmasters International is perhaps the best known public speaking organization in the world. Toastmasters is open to anyone who wish to improve their speaking skills and is willing to put in the time and effort to do so. To learn more, visit Toastmasters International .

Methods of Communication

Communication of problems and issues in any workplace is important, particularly when safety is involved. It is therefore crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. As issues and problems arise, they need to be addressed in an efficient and timely manner. Effective communication is an important skill because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money.

There are many different ways to communicate: in person, by phone, via email, or written. There is no single method that fits all communication needs, each one has its time and place.

In person: In the workplace, face-to-face meetings should be utilized whenever possible. Being able to see the person you need to speak to face-to-face gives you instant feedback and helps you gauge their response through their body language. Be careful of getting sidetracked in conversation when you need to communicate a problem.

Email: Email has become the communication standard for most businesses. It can be accessed from almost anywhere and is great for things that don’t require an immediate response. Email is a great way to communicate non-urgent items to large amounts of people or just your team members. One thing to remember is that most people's inboxes are flooded with emails every day and unless they are hyper vigilant about checking everything, important items could be missed. For issues that are urgent, especially those around safety, email is not always be the best solution.

Phone: Phone calls are more personal and direct than email. They allow us to communicate in real time with another person, no matter where they are. Not only can talking prevent miscommunication, it promotes a two-way dialogue. You don’t have to worry about your words being altered or the message arriving on time. However, mobile phone use and the workplace don't always mix. In particular, using mobile phones in a manufacturing setting can lead to a variety of problems, cause distractions, and lead to serious injury.

Written: Written communication is appropriate when detailed instructions are required, when something needs to be documented, or when the person is too far away to easily speak with over the phone or in person.

There is no "right" way to communicate, but you should be aware of how and when to use the appropriate form of communication for your situation. When deciding the best way to communicate with a co-worker or manager, put yourself in their shoes, and think about how you would want to learn about the issue. Also, consider what information you would need to know to better understand the issue. Use your good judgment of the situation and be considerate of your listener's viewpoint.

Did you notice any other potential problems in the previous exercise?

[Page 6:] Did you notice any other potential problems in the previous exercise?

Summary of Strategies

In this exercise, you were given a scenario in which there was a problem with a component you were creating on a CNC machine. You were then asked how you wanted to proceed. Depending on your path through this exercise, you might have found an easy solution and fixed it yourself, asked for help and worked with your trainer, or discovered an ongoing G-code problem that was bigger than you initially thought.

When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money. Although, each path in this exercise ended with a description of a problem solving tool for your toolbox, the first step is always to identify the problem and define the context in which it happened.

There are several strategies that can be used to identify the root cause of a problem. Root cause analysis (RCA) is a method of problem solving that helps people answer the question of why the problem occurred. RCA uses a specific set of steps, with associated tools like the “5 Why Analysis" or the “Cause and Effect Diagram,” to identify the origin of the problem, so that you can:

Once the underlying cause is identified and the scope of the issue defined, the next step is to explore possible strategies to fix the problem.

If you are not sure how to fix the problem, it is okay to ask for help. Problem solving is a process and a skill that is learned with practice. It is important to remember that everyone makes mistakes and that no one knows everything. Life is about learning. It is okay to ask for help when you don’t have the answer. When you collaborate to solve problems you improve workplace communication and accelerates finding solutions as similar problems arise.

One tool that can be useful for generating possible solutions is brainstorming . Brainstorming is a technique designed to generate a large number of ideas for the solution to a problem. The method was first popularized in 1953 by Alex Faickney Osborn in the book Applied Imagination. The goal is to come up with as many ideas as you can, in a fixed amount of time. Although brainstorming is best done in a group, it can be done individually.

Depending on your path through the exercise, you may have discovered that a couple of your coworkers had experienced similar problems. This should have been an indicator that there was a larger problem that needed to be addressed.

In any workplace, communication of problems and issues (especially those that involve safety) is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. When issues and problems arise, it is important that they be addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money.

One strategy for improving communication is the huddle . Just like football players on the field, a huddle is a short meeting with everyone standing in a circle. A daily team huddle is a great way to ensure that team members are aware of changes to the schedule, any problems or safety issues are identified and that team members are aware of how their work impacts one another. When done right, huddles create collaboration, communication, and accountability to results. Impromptu huddles can be used to gather information on a specific issue and get each team member's input.

To learn more about different problem solving strategies, choose an option below. These strategies accompany the outcomes of different decision paths in the problem solving exercise.

View Problem Solving Strategies Select a strategy below... Root Cause Analysis How Huddles Work Brainstorming Importance of Good Problem Description Methods of Communication

Provide a clear summary of the problem. Start at the beginning, give relevant facts, timelines, and examples.

In person: In the workplace, face-to-face meetings should be utilized whenever possible. Being able to see the person you need to speak to face-to-face gives you instant feedback and helps you gauge their response in their body language. Be careful of getting sidetracked in conversation when you need to communicate a problem.

There is no "right" way to communicate, but you should be aware of how and when to use the appropriate form of communication for the situation. When deciding the best way to communicate with a co-worker or manager, put yourself in their shoes, and think about how you would want to learn about the issue. Also, consider what information you would need to know to better understand the issue. Use your good judgment of the situation and be considerate of your listener's viewpoint.

"Never try to solve all the problems at once — make them line up for you one-by-one.” — Richard Sloma

Problem Solving: An Important Job Skill

Problem solving improves efficiency and communication on the shop floor. It increases a company's efficiency and profitability, so it's one of the top skills employers look for when hiring new employees. Recent industry surveys show that employers consider soft skills, such as problem solving, as critical to their business’s success.

The 2011 survey, "Boiling Point? The skills gap in U.S. manufacturing ," polled over a thousand manufacturing executives who reported that the number one skill deficiency among their current employees is problem solving, which makes it difficult for their companies to adapt to the changing needs of the industry.

In this video, industry professionals discuss their expectations and present tips for new employees joining the manufacturing workforce.

Quick Summary

[Quick Summary: Question1] What are two things you learned in this case study?
What question(s) do you still have about the case study?
[Quick Summary: Question2] What question(s) do you still have about the case study?
Is there anything you would like to learn more about with respect to this case study?
[Quick Summary: Question3] Is there anything you would like to learn more about with respect to this case study?

How to Train Your Problem-Solving Skills

From the hiccups that disrupt your morning routines to the hurdles that define your professional paths, there is always a problem to be solved.

The good news is that every obstacle is an opportunity to develop problem-solving skills and become the best version of yourself. That’s right: It turns out you can get better at problem-solving, which will help you increase success in daily life and long-term goals.

Read on to learn how to improve your problem-solving abilities through scientific research and practical strategies.

Understanding Problem-Solving Skills

You may be surprised to learn that your problem-solving skills go beyond just trying to find a solution. Problem-solving skills involve cognitive abilities such as analytical thinking, creativity, decision-making, logical reasoning, and memory.

Strong problem-solving skills boost critical thinking, spark creativity, and hone decision-making abilities. For you or anyone looking to improve their mental fitness , these skills are necessary for career advancement, personal growth, and positive interpersonal relationships.

Core Components of Problem-Solving Skills Training

To effectively train your problem-solving skills, it’s important to practice all of the steps required to solve the problem. Think of it this way: Before attempting to solve a problem, your brain has already been hard at work evaluating the situation and picking the best action plan. After you’ve worked hard preparing, you’ll need to implement your plan and assess the outcome by following these steps:

Identify and define problems: Recognizing and clearly articulating issues is the foundational step in solving them.
Generate solutions: Employing brainstorming techniques helps you develop multiple potential solutions.
Evaluate and select solutions: Using specific criteria to assess solutions helps you choose the most effective one.
Implement solutions: Developing and executing action plans, including preparing for potential obstacles, guides you to positive outcomes.
Review and learn from outcomes: Assessing the success of solutions and learning from the results for future improvement facilitates future success.

Strategies for Developing Problem-Solving Skills

There are many practical exercises and activities that can improve problem-solving abilities.

Cultivate a Problem-Solving Mindset

Adopt a growth mindset: A growth mindset involves transforming phrases like “I can’t” into “I can’t yet.” Believing in the capacity to improve your skills through effort and perseverance can lead to greater success in problem-solving.
Practice mindfulness: Mindfulness can enhance cognitive flexibility , allowing you to view problems from multiple perspectives and find creative solutions.

Enhance Core Cognitive Skills

Strengthen your memory: Engage in activities that challenge your memory since accurately recalling information is crucial in problem-solving. Techniques such as mnemonic devices or memory palaces can be particularly effective.
Build your critical thinking: Regularly question assumptions, evaluate arguments, and engage in activities that require reasoning, such as strategy games or debates.

Apply Structured Problem-Solving Techniques

Use the STOP method: This stands for Stop , Think , Observe , and Plan . It's a simple yet effective way to approach any problem methodically, ensuring you consider all aspects before taking action.
Try reverse engineering: Start with the desired outcome and work backward to understand the steps needed to achieve that result. This approach can be particularly useful for complex problems with unclear starting points.

Incorporate Technology into Your Training

Engage with online courses and workshops: Many platforms offer courses specifically designed to enhance problem-solving skills, ranging from critical thinking to creative problem-solving techniques.
Use cognitive training apps: Apps like Elevate provide targeted, research-backed games and workouts to improve cognitive skills including attention, processing speed, and more.

Practice with Real-World Applications and Learn from Experience

Tackle daily challenges: Use everyday issues as opportunities to practice problem-solving. Whether figuring out a new recipe or managing a tight budget, applying your skills in real-world situations can reinforce learning.
Keep a problem-solving journal: Record the challenges you face, the strategies you employ, and the outcomes you achieve. Reflecting on your problem-solving process over time can provide insights into your strengths and areas for improvement.

Embracing Problem-Solving as a Lifelong Journey

Since problems arise daily, it’s important to feel confident in solving them.

And you can do just that by downloading the Elevate brain training app. Elevate offers 40+ games and activities designed to improve problem-solving, communication, and other cognitive skills in a personalized way that’s backed by science. Pretty cool, right?

Consider downloading the Elevate app on Android or iOS now—it’ll be the easiest problem you solve all day.

How Problem-Solving Games Can Boost Your Brain

Discover why problem-solving games are fun and effective ways to train your brain.

Improving Your Problem-Solving Skills

Discover how to improve your problem-solving skills and make logical, informed decisions.

Best Ways to Boost Your Mental Fitness

Mental fitness refers to your ability to sustain your overall well-being. Learn tips to improve yours.

STEM Problem Solving: Inquiry, Concepts, and Reasoning

Published: 29 January 2022
Volume 32 , pages 381–397, ( 2023 )

Cite this article

Aik-Ling Tan ORCID: orcid.org/0000-0002-4627-4977 1 ,
Yann Shiou Ong ORCID: orcid.org/0000-0002-6092-2803 1 ,
Yong Sim Ng ORCID: orcid.org/0000-0002-8400-2040 1 &
Jared Hong Jie Tan 1

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Balancing disciplinary knowledge and practical reasoning in problem solving is needed for meaningful learning. In STEM problem solving, science subject matter with associated practices often appears distant to learners due to its abstract nature. Consequently, learners experience difficulties making meaningful connections between science and their daily experiences. Applying Dewey’s idea of practical and science inquiry and Bereiter’s idea of referent-centred and problem-centred knowledge, we examine how integrated STEM problem solving offers opportunities for learners to shuttle between practical and science inquiry and the kinds of knowledge that result from each form of inquiry. We hypothesize that connecting science inquiry with practical inquiry narrows the gap between science and everyday experiences to overcome isolation and fragmentation of science learning. In this study, we examine classroom talk as students engage in problem solving to increase crop yield. Qualitative content analysis of the utterances of six classes of 113 eighth graders and their teachers were conducted for 3 hours of video recordings. Analysis showed an almost equal amount of science and practical inquiry talk. Teachers and students applied their everyday experiences to generate solutions. Science talk was at the basic level of facts and was used to explain reasons for specific design considerations. There was little evidence of higher-level scientific conceptual knowledge being applied. Our observations suggest opportunities for more intentional connections of science to practical problem solving, if we intend to apply higher-order scientific knowledge in problem solving. Deliberate application and reference to scientific knowledge could improve the quality of solutions generated.

Social Learning Theory—Albert Bandura

Exploring the factors that influence the career decision of STEM students at a university in South Africa

Ethel Ndidiamaka Abe & Vitallis Chikoko

Opportunities and Challenges of STEM Education

Avoid common mistakes on your manuscript.

1 Introduction

As we enter to second quarter of the twenty-first century, it is timely to take stock of both the changes and demands that continue to weigh on our education system. A recent report by World Economic Forum highlighted the need to continuously re-position and re-invent education to meet the challenges presented by the disruptions brought upon by the fourth industrial revolution (World Economic Forum, 2020 ). There is increasing pressure for education to equip children with the necessary, relevant, and meaningful knowledge, skills, and attitudes to create a “more inclusive, cohesive and productive world” (World Economic Forum, 2020 , p. 4). Further, the shift in emphasis towards twenty-first century competencies over mere acquisition of disciplinary content knowledge is more urgent since we are preparing students for “jobs that do not yet exist, technology that has not yet been invented, and problems that has yet exist” (OECD, 2018 , p. 2). Tan ( 2020 ) concurred with the urgent need to extend the focus of education, particularly in science education, such that learners can learn to think differently about possibilities in this world. Amidst this rhetoric for change, the questions that remained to be answered include how can science education transform itself to be more relevant; what is the role that science education play in integrated STEM learning; how can scientific knowledge, skills and epistemic practices of science be infused in integrated STEM learning; what kinds of STEM problems should we expose students to for them to learn disciplinary knowledge and skills; and what is the relationship between learning disciplinary content knowledge and problem solving skills?

In seeking to understand the extent of science learning that took place within integrated STEM learning, we dissected the STEM problems that were presented to students and examined in detail the sense making processes that students utilized when they worked on the problems. We adopted Dewey’s ( 1938 ) theoretical idea of scientific and practical/common-sense inquiry and Bereiter’s ideas of referent-centred and problem-centred knowledge building process to interpret teacher-students’ interactions during problem solving. There are two primary reasons for choosing these two theoretical frameworks. Firstly, Dewey’s ideas about the relationship between science inquiry and every day practical problem-solving is important in helping us understand the role of science subject matter knowledge and science inquiry in solving practical real-world problems that are commonly used in STEM learning. Secondly, Bereiter’s ideas of referent-centred and problem-centred knowledge augment our understanding of the types of knowledge that students can learn when they engage in solving practical real-world problems.

Taken together, Dewey’s and Bereiter’s ideas enable us to better understand the types of problems used in STEM learning and their corresponding knowledge that is privileged during the problem-solving process. As such, the two theoretical lenses offered an alternative and convincing way to understand the actual types of knowledge that are used within the context of integrated STEM and help to move our understanding of STEM learning beyond current focus on examining how engineering can be used as an integrative mechanism (Bryan et al., 2016 ) or applying the argument of the strengths of trans-, multi-, or inter-disciplinary activities (Bybee, 2013 ; Park et al., 2020 ) or mapping problems by the content and context as pure STEM problems, STEM-related problems or non-STEM problems (Pleasants, 2020 ). Further, existing research (for example, Gale et al., 2000 ) around STEM education focussed largely on description of students’ learning experiences with insufficient attention given to the connections between disciplinary conceptual knowledge and inquiry processes that students use to arrive at solutions to problems. Clarity in the role of disciplinary knowledge and the related inquiry will allow for more intentional design of STEM problems for students to learn higher-order knowledge. Applying Dewey’s idea of practical and scientific inquiry and Bereiter’s ideas of referent-centred and problem-centred knowledge, we analysed six lessons where students engaged with integrated STEM problem solving to propose answers to the following research questions: What is the extent of practical and scientific inquiry in integrated STEM problem solving? and What conceptual knowledge and problem-solving skills are learnt through practical and science inquiry during integrated STEM problem solving?

2 Inquiry in Problem Solving

Inquiry, according to Dewey ( 1938 ), involves the direct control of unknown situations to change them into a coherent and unified one. Inquiry usually encompasses two interrelated activities—(1) thinking about ideas related to conceptual subject-matter and (2) engaging in activities involving our senses or using specific observational techniques. The National Science Education Standards released by the National Research Council in the US in 1996 defined inquiry as “…a multifaceted activity that involves making observations; posing questions; examining books and other sources of information to see what is already known; planning investigations; reviewing what is already known in light of experimental evidence; using tools to gather, analyze, and interpret data; proposing answers, explanations, and predictions; and communicating the results. Inquiry requires identification of assumptions, use of critical and logical thinking, and consideration of alternative explanations” (p. 23). Planning investigation; collecting empirical evidence; using tools to gather, analyse and interpret data; and reasoning are common processes shared in the field of science and engineering and hence are highly relevant to apply to integrated STEM education.

In STEM education, establishing the connection between general inquiry and its application helps to link disciplinary understanding to epistemic knowledge. For instance, methods of science inquiry are popular in STEM education due to the familiarity that teachers have with scientific methods. Science inquiry, a specific form of inquiry, has appeared in many science curriculum (e.g. NRC, 2000 ) since Dewey proposed in 1910 that learning of science should be perceived as both subject-matter and a method of learning science (Dewey, 1910a , 1910b ). Science inquiry which involved ways of doing science should also encompass the ways in which students learn the scientific knowledge and investigative methods that enable scientific knowledge to be constructed. Asking scientifically orientated questions, collecting empirical evidence, crafting explanations, proposing models and reasoning based on available evidence are affordances of scientific inquiry. As such, science should be pursued as a way of knowing rather than merely acquisition of scientific knowledge.

Building on these affordances of science inquiry, Duschl and Bybee ( 2014 ) advocated the 5D model that focused on the practice of planning and carrying out investigations in science and engineering, representing two of the four disciplines in STEM. The 5D model includes science inquiry aspects such as (1) deciding on what and how to measure, observe and sample; (2) developing and selecting appropriate tools to measure and collect data; (3) recording the results and observations in a systematic manner; (4) creating ways to represent the data and patterns that are observed; and (5) determining the validity and the representativeness of the data collected. The focus on planning and carrying out investigations in the 5D model is used to help teachers bridge the gap between the practices of building and refining models and explanation in science and engineering. Indeed, a common approach to incorporating science inquiry in integrated STEM curriculum involves student planning and carrying out scientific investigations and making sense of the data collected to inform engineering design solution (Cunningham & Lachapelle, 2016 ; Roehrig et al., 2021 ). Duschl and Bybee ( 2014 ) argued that it is needful to design experiences for learners to appreciate that struggles are part of problem solving in science and engineering. They argued that “when the struggles of doing science is eliminated or simplified, learners get the wrong perceptions of what is involved when obtaining scientific knowledge and evidence” (Duschl & Bybee, 2014 , p. 2). While we concur with Duschl and Bybee about the need for struggles, in STEM learning, these struggles must be purposeful and grade appropriate so that students will also be able to experience success amidst failure.

The peculiar nature of science inquiry was scrutinized by Dewey ( 1938 ) when he cross-examined the relationship between science inquiry and other forms of inquiry, particularly common-sense inquiry. He positioned science inquiry along a continuum with general or common-sense inquiry that he termed as “logic”. Dewey argued that common-sense inquiry serves a practical purpose and exhibits features of science inquiry such as asking questions and a reliance on evidence although the focus of common-sense inquiry tends to be different. Common-sense inquiry deals with issues or problems that are in the immediate environment where people live, whereas the objects of science inquiry are more likely to be distant (e.g. spintronics) from familiar experiences in people’s daily lives. While we acknowledge the fundamental differences (such as novel discovery compared with re-discovering science, ‘messy’ science compared with ‘sanitised’ science) between school science and science that is practiced by scientists, the subject of interest in science (understanding the world around us) remains the same.

The unfamiliarity between the functionality and purpose of science inquiry to improve the daily lives of learners does little to motivate learners to learn science (Aikenhead, 2006 ; Lee & Luykx, 2006 ) since learners may not appreciate the connections of science inquiry in their day-to-day needs and wants. Bereiter ( 1992 ) has also distinguished knowledge into two forms—referent-centred and problem-centred. Referent-centred knowledge refers to subject-matter that is organised around topics such as that in textbooks. Problem-centred knowledge is knowledge that is organised around problems, whether they are transient problems, practical problems or problems of explanations. Bereiter argued that referent-centred knowledge that is commonly taught in schools is limited in their applications and meaningfulness to the lives of students. This lack of familiarity and affinity to referent-centred knowledge is likened to the science subject-matter knowledge that was mentioned by Dewey. Rather, it is problem-centred knowledge that would be useful when students encounter problems. Learning problem-centred knowledge will allow learners to readily harness the relevant knowledge base that is useful to understand and solve specific problems. This suggests a need to help learners make the meaningful connections between science and their daily lives.

Further, Dewey opined that while the contexts in which scientific knowledge arise could be different from our daily common-sense world, careful consideration of scientific activities and applying the resultant knowledge to daily situations for use and enjoyment is possible. Similarly, in arguing for problem-centred knowledge, Bereiter ( 1992 ) questioned the value of inert knowledge that plays no role in helping us understand or deal with the world around us. Referent-centred knowledge has a higher tendency to be inert due to the way that the knowledge is organised and the way that the knowledge is encountered by learners. For instance, learning about the equation and conditions for photosynthesis is not going to help learners appreciate how plants are adapted for photosynthesis and how these adaptations can allow plants to survive changes in climate and for farmers to grow plants better by creating the best growing conditions. Rather, students could be exposed to problems of explanations where they are asked to unravel the possible reasons for low crop yield and suggest possible ways to overcome the problem. Hence, we argue here that the value of the referent knowledge is that they form the basis and foundation for the students to be able to discuss or suggest ways to overcome real life problems. Referent-centred knowledge serves as part of the relevant knowledge base that can be harnessed to solve specific problems or as foundational knowledge students need to progress to learn higher-order conceptual knowledge that typically forms the foundations or pillars within a discipline. This notion of referent-centred knowledge serving as foundational knowledge that can be and should be activated for application in problem-solving situation is shown by Delahunty et al. ( 2020 ). They found that students show high reliance on memory when they are conceptualising convergent problem-solving tasks.

While Bereiter argues for problem-centred knowledge, he cautioned that engagement should be with problems of explanation rather than transient or practical problems. He opined that if learners only engage in transient or practical problem alone, they will only learn basic-category types of knowledge and fail to understand higher-order conceptual knowledge. For example, for photosynthesis, basic-level types of knowledge included facts about the conditions required for photosynthesis, listing the products formed from the process of photosynthesis and knowing that green leaves reflect green light. These basic-level knowledges should intentionally help learners learn higher-level conceptual knowledge that include learners being able to draw on the conditions for photosynthesis when they encounter that a plant is not growing well or is exhibiting discoloration of leaves.

Transient problems disappear once a solution becomes available and there is a high likelihood that we will not remember the problem after that. Practical problems, according to Bereiter are “stuck-door” problems that could be solved with or without basic-level knowledge and often have solutions that lacks precise definition. There are usually a handful of practical strategies, such as pulling or pushing the door harder, kicking the door, etc. that will work for the problems. All these solutions lack a well-defined approach related to general scientific principles that are reproducible. Problems of explanations are the most desirable types of problems for learners since these are problems that persist and recur such that they can become organising points for knowledge. Problems of explanations consist of the conceptual representations of (1) a text base that serves to represent the text content and (2) a situation model that shows the portion of the world in which the text is relevant. The idea of text base to represent text content in solving problems of explanations is like the idea of domain knowledge and structural knowledge (refers to knowledge of how concepts within a domain are connected) proposed by Jonassen ( 2000 ). He argued that both types of knowledges are required to solve a range of problems from well-structured problems to ill-structured problems with a simulated context, to simple ill-structured problems and to complex ill-structured problems.

Jonassen indicated that complex ill-structured problems are typically design problems and are likely to be the most useful forms of problems for learners to be engaged in inquiry. Complex ill-structured design problems are the “wicked” problems that Buchanan ( 1992 ) discussed. Buchanan’s idea is that design aims to incorporate knowledge from different fields of specialised inquiry to become whole. Complex or wicked problems are akin to the work of scientists who navigate multiple factors and evidence to offer models that are typically oversimplified, but they apply them to propose possible first approximation explanations or solutions and iteratively relax constraints or assumptions to refine the model. The connections between the subject matter of science and the design process to engineer a solution are delicate. While it is important to ensure that practical concerns and questions are taken into consideration in designing solutions (particularly a material artefact) to a practical problem, the challenge here lies in ensuring that creativity in design is encouraged even if students initially lack or neglect the scientific conceptual understanding to explain/justify their design. In his articulation of wicked problems and the role of design thinking, Buchanan ( 1992 ) highlighted the need to pay attention to category and placement. Categories “have fixed meanings that are accepted within the framework of a theory or a philosophy and serve as the basis for analyzing what already exist” (Buchanan, 1992 , p. 12). Placements, on the other hand, “have boundaries to shape and constrain meaning, but are not rigidly fixed and determinate” (p. 12).

The difference in the ideas presented by Dewey and Bereiter lies in the problem design. For Dewey, scientific knowledge could be learnt from inquiring into practical problems that learners are familiar with. After all, Dewey viewed “modern science as continuous with, and to some degree an outgrowth and refinement of, practical or ‘common-sense’ inquiry” (Brown, 2012 ). For Bereiter, he acknowledged the importance of familiar experiences, but instead of using them as starting points for learning science, he argued that practical problems are limiting in helping learners acquire higher-order knowledge. Instead, he advocated for learners to organize their knowledge around problems that are complex, persistent and extended and requiring explanations to better understand the problems. Learners are to have a sense of the kinds of problems to which the specific concept is relevant before they can be said to have grasp the concept in a functionally useful way.

To connect between problem solving, scientific knowledge and everyday experiences, we need to examine ways to re-negotiate the disciplinary boundaries (such as epistemic understanding, object of inquiry, degree of precision) of science and make relevant connections to common-sense inquiry and to the problem at hand. Integrated STEM appears to be one way in which the disciplinary boundaries of science can be re-negotiated to include practices from the fields of technology, engineering and mathematics. In integrated STEM learning, inquiry is seen more holistically as a fluid process in which the outcomes are not absolute but are tentative. The fluidity of the inquiry process is reflected in the non-deterministic inquiry approach. This means that students can use science inquiry, engineering design, design process or any other inquiry approaches that fit to arrive at the solution. This hybridity of inquiry between science, common-sense and problems allows for some familiar aspects of the science inquiry process to be applied to understand and generate solutions to familiar everyday problems. In attempting to infuse elements of common-sense inquiry with science inquiry in problem-solving, logic plays an important role to help learners make connections. Hypothetically, we argue that with increasing exposure to less familiar ways of thinking such as those associated with science inquiry, students’ familiarity with scientific reasoning increases, and hence such ways of thinking gradually become part of their common-sense, which students could employ to solve future relevant problems. The theoretical ideas related to complexities of problems, the different forms of inquiry afforded by different problems and the arguments for engaging in problem solving motivated us to examine empirically how learners engage with ill-structured problems to generate problem-centred knowledge. Of particular interest to us is how learners and teachers weave between practical and scientific reasoning as they inquire to integrate the components in the original problem into a unified whole.

3.1 Context

The integrated STEM activity in our study was planned using the S-T-E-M quartet instructional framework (Tan et al., 2019 ). The S-T-E-M quartet instructional framework positions complex, persistent and extended problems at its core and focusses on the vertical disciplinary knowledge and understanding of the horizontal connections between the disciplines that could be gained by learners through solving the problem (Tan et al., 2019 ). Figure 1 depicts the disciplinary aspects of the problem that was presented to the students. The activity has science and engineering as the two lead disciplines. It spanned three 1-h lessons and required students to both learn and apply relevant scientific conceptual knowledge to solve a complex, real-world problem through processes that resemble the engineering design process (Wheeler et al., 2019 ).

Connections across disciplines in integrate STEM activity

Frequency of different types of reasoning

In the first session (1 h), students were introduced to the problem and its context. The problem pertains to the issue of limited farmland in a land scarce country that imports 90% of food (Singapore Food Agency [SFA], 2020 ). The students were required to devise a solution by applying knowledge of the conditions required for photosynthesis and plant growth to design and build a vertical farming system to help farmers increase crop yield with limited farmland. This context was motivated by the government’s effort to generate interests and knowledge in farming to achieve the 30 by 30 goal—supplying 30% of country’s nutritional needs by 2030. The scenario was a fictitious one where they were asked to produce 120 tonnes of Kailan (a type of leafy vegetable) with two hectares of land instead of the usual six hectares over a specific period. In addition to the abovementioned constraints, the teacher also discussed relevant success criteria for evaluating the solution with the students. Students then researched about existing urban farming approaches. They were given reading materials pertaining to urban farming to help them understand the affordances and constraints of existing solutions. In the second session (6 h), students engaged in ideation to generate potential solutions. They then designed, built and tested their solution and had opportunities to iteratively refine their solution. Students were given a list of materials (e.g. mounting board, straws, ice-cream stick, glue, etc.) that they could use to design their solutions. In the final session (1 h), students presented their solution and reflected on how well their solution met the success criteria. The prior scientific conceptual knowledge that students require to make sense of the problem include knowledge related to plant nutrition, namely, conditions for photosynthesis, nutritional requirements of Kailin and growth cycle of Kailin. The problem resembles a real-world problem that requires students to engage in some level of explanation of their design solution.

A total of 113 eighth graders (62 boys and 51 girls), 14-year-olds, from six classes and their teachers participated in the study. The students and their teachers were recruited as part of a larger study that examined the learning experiences of students when they work on integrated STEM activities that either begin with a problem, a solution or are focused on the content. Invitations were sent to schools across the country and interested schools opted in for the study. For the study reported here, all students and teachers were from six classes within a school. The teachers had all undergone 3 h of professional development with one of the authors on ways of implementing the integrated STEM activity used in this study. During the professional development session, the teachers learnt about the rationale of the activity, familiarize themselves with the materials and clarified the intentions and goals of the activity. The students were mostly grouped in groups of three, although a handful of students chose to work independently. The group size of students was not critical for the analysis of talk in this study as the analytic focus was on the kinds of knowledge applied rather than collaborative or group think. We assumed that the types of inquiry adopted by teachers and students were largely dependent on the nature of problem. Eighth graders were chosen for this study since lower secondary science offered at this grade level is thematic and integrated across biology, chemistry and physics. Furthermore, the topic of photosynthesis is taught under the theme of Interactions at eighth grade (CPDD, 2021 ). This thematic and integrated nature of science at eighth grade offered an ideal context and platform for integrated STEM activities to be trialled.

The final lessons in a series of three lessons in each of the six classes was analysed and reported in this study. Lessons where students worked on their solutions were not analysed because the recordings had poor audibility due to masking and physical distancing requirements as per COVID-19 regulations. At the start of the first lesson, the instructions given by the teacher were:

You are going to present your models. Remember the scenario that you were given at the beginning that you were tasked to solve using your model. …. In your presentation, you have to present your prototype and its features, what is so good about your prototype, how it addresses the problem and how it saves costs and space. So, this is what you can talk about during your presentation. ….. pay attention to the presentation and write down questions you like to ask the groups after the presentation… you can also critique their model, you can evaluate, critique and ask questions…. Some examples of questions you can ask the groups are? Do you think your prototype can achieve optimal plant growth? You can also ask questions specific to their models.

3.2 Data collection

Parental consent was sought a month before the start of data collection. The informed consent adhered to confidentiality and ethics guidelines as described by the Institutional Review Board. The data collection took place over a period of one month with weekly video recording. Two video cameras, one at the front and one at the back of the science laboratory were set up. The front camera captured the students seated at the front while the back video camera recorded the teacher as well as the groups of students at the back of the laboratory. The video recordings were synchronized so that the events captured from each camera can be interpreted from different angles. After transcription of the raw video files, the identities of students were substituted with pseudonyms.

3.3 Data analysis

The video recordings were analysed using the qualitative content analysis approach. Qualitative content analysis allows for patterns or themes and meanings to emerge from the process of systematic classification (Hsieh & Shannon, 2005 ). Qualitative content analysis is an appropriate analytic method for this study as it allows us to systematically identify episodes of practical inquiry and science inquiry to map them to the purposes and outcomes of these episodes as each lesson unfolds.

In total, six h of video recordings where students presented their ideas while the teachers served as facilitator and mentor were analysed. The video recordings were transcribed, and the transcripts were analysed using the NVivo software. Our unit of analysis is a single turn of talk (one utterance). We have chosen to use utterances as proxy indicators of reasoning practices based on the assumption that an utterance relates to both grammar and context. An utterance is a speech act that reveals both meaning and intentions of the speaker within specific contexts (Li, 2008 ).

Our research analytical lens is also interpretative in nature and the validity of our interpretation is through inter-rater discussion and agreement. Each utterance at the speaker level in transcripts was examined and coded either as relevant to practical reasoning or scientific reasoning based on the content. The utterances could be a comment by the teacher, a question by a student or a response by another student. Deductive coding is deployed with the two codes, practical reasoning and scientific reasoning derived from the theoretical ideas of Dewey and Bereiter as described earlier. Practical reasoning refers to utterances that reflect commonsensical knowledge or application of everyday understanding. Scientific reasoning refers to utterances that consist of scientifically oriented questions, scientific terms, or the use of empirical evidence to explain. Examples of each type of reasoning are highlighted in the following section. Each coded utterance is then reviewed for detailed description of the events that took place that led to that specific utterance. The description of the context leading to the utterance is considered an episode. The episodes and codes were discussed and agreed upon by two of the authors. Two coders simultaneously watched the videos to identify and code the episodes. The coders interpreted the content of each utterance, examine the context where the utterance was made and deduced the purpose of the utterance. Once each coder has established the sense-making aspect of the utterance in relation to the context, a code of either practical reasoning or scientific reasoning is assigned. Once that was completed, the two coders compared their coding for similarities and differences. They discussed the differences until an agreement was reached. Through this process, an agreement of 85% was reached between the coders. Where disagreement persisted, codes of the more experienced coder were adopted.

4 Results and Discussion

The specific STEM lessons analysed were taken from the lessons whereby students presented the model of their solutions to the class for peer evaluation. Every group of students stood in front of the class and placed their model on the bench as they presented. There was also a board where they could sketch or write their explanations should they want to. The instructions given by the teacher to the students were to explain their models and state reasons for their design.

4.1 Prevalence of Reasoning

The 6h of videos consists of 1422 turns of talk. Three hundred four turns of talk (21%) were identified as talk related to reasoning, either practical reasoning or scientific reasoning. Practical reasoning made up 62% of the reasoning turns while 38% were scientific reasoning (Fig. 2 ).

The two types of reasoning differ in the justifications that are used to substantiate the claims or decisions made. Table 1 describes the differences between the two categories of reasoning.

4.2 Applications of Scientific Reasoning

Instances of engagement with scientific reasoning (for instance, using scientific concepts to justify, raising scientifically oriented questions, or providing scientific explanations) revolved around the conditions for photosynthesis and the concept of energy conversion when students were presenting their ideas or when they were questioned by their peers. For example, in explaining the reason for including fish in their plant system, one group of students made connection to cyclical energy transfer: “…so as the roots of the plants submerged in the water, faeces from the fish will be used as fertilizers so that the plant can grow”. The students considered how organic matter that is still trapped within waste materials can be released and taken up by plants to enhance the growth. The application of scientific reasoning made their design one that is innovative and sustainable as evaluated by the teacher. Some students attempted more ecofriendly designs by considering energy efficiencies through incorporating water turbines in their farming systems. They applied the concept of different forms of energy and energy conversion when their peers inquired about their design. The same scientific concepts were explained at different levels of details by different students. At one level, the students explained in a purely descriptive manner of what happens to the different entities in their prototypes, with implied changes to the forms of energy─ “…spins then generates electricity. So right, when the water falls down, then it will spin. The water will fall on the fan blade thing, then it will spin and then it generates electricity. So, it saves electricity, and also saves water”. At another level, students defended their design through an explanation of energy conversion─ “…because when the water flows right, it will convert gravitational potential energy so, when it reaches the bottom, there is not really much gravitational potential energy”. While these instances of applying scientific reasoning indicated that students have knowledge about the scientific phenomena and can apply them to assist in the problem-solving process, we are not able to establish if students understood the science behind how the dynamo works to generate electricity. Students in eighth grade only need to know how a generator works at a descriptive level and the specialized understanding how a dynamo works is beyond the intended learning outcomes at this grade level.

The application of scientific concepts for justification may not always be accurate. For instance, the naïve conception that students have about plants only respiring at night and not in the day surfaced when one group of students tried to justify the growth rates of Kailan─ “…I mean, they cannot be making food 24/7 and growing 24/7. They have nighttime for a reason. They need to respire”. These students do not appreciate that plants respire in the day as well, and hence respiration occurs 24/7. This naïve conception that plants only respire at night is one that is common among learners of biology (e.g. Svandova, 2014 ) since students learn that plant gives off oxygen in the day and takes in oxygen at night. The hasty conclusion to that observation is that plants carry out photosynthesis in the day and respire at night. The relative rates of photosynthesis and respiration were not considered by many students.

Besides naïve conceptions, engagement with scientific ideas to solve a practical problem offers opportunities for unusual and alternative ideas about science to surface. For instance, another group of students explained that they lined up their plants so that “they can take turns to absorb sunlight for photosynthesis”. These students appear to be explaining that the sun will move and depending on the position of the sun, some plants may be under shade, and hence rates of photosynthesis are dependent on the position of the sun. However, this idea could also be interpreted as (1) the students failed to appreciate that sunlight is everywhere, and (2) plants, unlike animals, particularly humans, do not have the concept of turn-taking. These diverse ideas held by students surfaced when students were given opportunities to apply their knowledge of photosynthesis to solve a problem.

4.3 Applications of Practical Reasoning

Teachers and students used more practical reasoning during an integrated STEM activity requiring both science and engineering practices as seen from 62% occurrence of practical reasoning compared with 38% for scientific reasoning. The intention of the activity to integrate students’ scientific knowledge related to plant nutrition to engineering practice of building a model of vertical farming system could be the reason for the prevalence of practical reasoning. The practical reasoning used related to structural design considerations of the farming system such as how water, light and harvesting can be carried out in the most efficient manner. Students defended the strengths of designs using logic based on their everyday experiences. In the excerpt below (transcribed verbatim), we see students applied their everyday experiences when something is “thinner” (likely to mean narrower), logically it would save space. Further, to reach a higher level, you use a machine to climb up.

Excerpt 1. “Thinner, more space” Because it is more thinner, so like in terms of space, it’s very convenient. So right, because there is – because it rotates right, so there is this button where you can stop it. Then I also installed steps, so that – because there are certain places you can’t reach even if you stop the – if you stop the machine, so when you stop it and you climb up, and then you see the condition of the plants, even though it costs a lot of labour, there is a need to have an experienced person who can grow plants. Then also, when like – when water reach the plants, cos the plants I want to use is soil-based, so as the water reach the soil, the soil will xxx, so like the water will be used, and then we got like – and then there’s like this filter that will filter like the dirt.

In the examples of practical reasoning, we were not able to identify instances where students and teachers engaged with discussion around trade-off and optimisation. Understanding constraints, trade-offs and optimisations are important ideas in informed design matrix for engineering as suggested by Crismond and Adams ( 2012 ). For instance, utterances such as “everything will be reused”, “we will be saving space”, “it looks very flimsy” or “so that it can contains [sic] the plants” were used. These utterances were made both by students while justifying their own prototypes and also by peers who challenged the design of others. Longer responses involving practical reasoning were made based on common-sense, everyday logic─ “…the product does not require much manpower, so other than one or two supervisors like I said just now, to harvest the Kailan, hence, not too many people need to be used, need to be hired to help supervise the equipment and to supervise the growth”. We infer that the higher instances of utterances related to practical reasoning could be due to the presence of more concrete artefacts that is shown, and the students and teachers were more focused on questioning the structure at hand. This inference was made as instructions given by the teacher at the start of students’ presentation focus largely on the model rather than the scientific concepts or reasoning behind the model.

4.4 Intersection Between Scientific and Practical Reasoning

Comparing science subject matter knowledge and problem-solving to the idea of categories and placement (Buchanan, 1992 ), subject matter is analogous to categories where meanings are fixed with well-established epistemic practices and norms. The problem-solving process and design of solutions are likened to placements where boundaries are less rigid, hence opening opportunities for students’ personal experiences and ideas to be presented. Placements allow students to apply their knowledge from daily experiences and common-sense logic to justify decisions. Common-sense knowledge and logic are more accessible, and hence we observe higher frequency of usage. Comparatively, while science subject matter (categories) is also used, it is observed less frequently. This could possibly be due either to less familiarity with the subject matter or lack of appropriate opportunity to apply in practical problem solving. The challenge for teachers during implementation of a STEM problem-solving activity, therefore, lies in the balance of the application of scientific and practical reasoning to deepen understanding of disciplinary knowledge in the context of solving a problem in a meaningful manner.

Our observations suggest that engaging students with practical inquiry tasks with some engineering demands such as the design of modern farm systems offers opportunities for them to convert their personal lived experiences into feasible concrete ideas that they can share in a public space for critique. The peer critique following the sharing of their practical ideas allows for both practical and scientific questions to be asked and for students to defend their ideas. For instance, after one group of students presented their prototype that has silvered surfaces, a student asked a question: “what is the function of the silver panels?”, to which his peers replied : “Makes the light bounce. Bounce the sunlight away and then to other parts of the tray.” This question indicated that students applied their knowledge that shiny silvered surfaces reflect light, and they used this knowledge to disperse the light to other trays where the crops were growing. An example of a practical question asked was “what is the purpose of the ladder?”, to which the students replied: “To take the plants – to refill the plants, the workers must climb up”. While the process of presentation and peer critique mimic peer review in the science inquiry process, the conceptual knowledge of science may not always be evident as students paid more attention to the design constraints such as lighting, watering, and space that was set in the activity. Given the context of growing plants, engagement with the science behind nutritional requirements of plants, the process of photosynthesis, and the adaptations of plants could be more deliberately explored.

5 Conclusion

The goal of our work lies in applying the theoretical ideas of Dewey and Bereiter to better understand reasoning practices in integrate STEM problem solving. We argue that this is a worthy pursue to better understand the roles of scientific reasoning in practical problem solving. One of the goals of integrated STEM education in schools is to enculture students into the practices of science, engineering and mathematics that include disciplinary conceptual knowledge, epistemic practices, and social norms (Kelly & Licona, 2018 ). In the integrated form, the boundaries and approaches to STEM learning are more diverse compared with monodisciplinary ways of problem solving. For instance, in integrated STEM problem solving, besides scientific investigations and explanations, students are also required to understand constraints, design optimal solutions within specific parameters and even to construct prototypes. For students to learn the ways of speaking, doing and being as they participate in integrated STEM problem solving in schools in a meaningful manner, students could benefit from these experiences.

With reference to the first research question of What is the extent of practical and scientific reasoning in integrated STEM problem solving, our analysis suggests that there are fewer instances of scientific reasoning compared with practical reasoning. Considering the intention of integrated STEM learning and adopting Bereiter’s idea that students should learn higher-order conceptual knowledge through engagement with problem solving, we argue for a need for scientific reasoning to be featured more strongly in integrated STEM lessons so that students can gain higher order scientific conceptual knowledge. While the lessons observed were strong in design and building, what was missing in generating solutions was the engagement in investigations, where learners collected or are presented with data and make decisions about the data to allow them to assess how viable the solutions are. Integrated STEM problems can be designed so that science inquiry can be infused, such as carrying out investigations to figure out relationships between variables. Duschl and Bybee ( 2014 ) have argued for the need to engage students in problematising science inquiry and making choices about what works and what does not.

With reference to the second research question , What is achieved through practical and scientific reasoning during integrated STEM problem solving? , our analyses suggest that utterance for practical reasoning are typically used to justify the physical design of the prototype. These utterances rely largely on what is observable and are associated with basic-level knowledge and experiences. The higher frequency of utterances related to practical reasoning and the nature of the utterances suggests that engagement with practical reasoning is more accessible since they relate more to students’ lived experiences and common-sense. Bereiter ( 1992 ) has urged educators to engage learners in learning that is beyond basic-level knowledge since accumulation of basic-level knowledge does not lead to higher-level conceptual learning. Students should be encouraged to use scientific knowledge also to justify their prototype design and to apply scientific evidence and logic to support their ideas. Engagement with scientific reasoning is preferred as conceptual knowledge, epistemic practices and social norms of science are more widely recognised compared with practical reasoning that are likely to be more varied since they rely on personal experiences and common-sense. This leads us to assert that both context and content are important in integrated STEM learning. Understanding the context or the solution without understanding the scientific principles that makes it work makes the learning less meaningful since we “…cannot strip learning of its context, nor study it in a ‘neutral’ context. It is always situated, always relayed to some ongoing enterprise”. (Bruner, 2004 , p. 20).

To further this discussion on how integrated STEM learning experiences harness the ideas of practical and scientific reasoning to move learners from basic-level knowledge to higher-order conceptual knowledge, we propose the need for further studies that involve working with teachers to identify and create relevant problems-of-explanations that focuses on feasible, worthy inquiry ideas such as those related to specific aspects of transportation, alternative energy sources and clean water that have impact on the local community. The design of these problems can incorporate opportunities for systematic scientific investigations and scaffolded such that there are opportunities to engage in epistemic practices of the constitute disciplines of STEM. Researchers could then examine the impact of problems-of-explanations on students’ learning of higher order scientific concepts. During the problem-solving process, more attention can be given to elicit students’ initial and unfolding ideas (practical) and use them as a basis to start the science inquiry process. Researchers can examine how to encourage discussions that focus on making meaning of scientific phenomena that are embedded within specific problems. This will help students to appreciate how data can be used as evidence to support scientific explanations as well as justifications for the solutions to problems. With evidence, learners can be guided to work on reasoning the phenomena with explanatory models. These aspects should move engagement in integrated STEM problem solving from being purely practice to one that is explanatory.

6 Limitations

There are four key limitations of our study. Firstly, the degree of generalisation of our observations is limited. This study sets out to illustrate what how Dewey and Bereiter’s ideas can be used as lens to examine knowledge used in problem-solving. As such, the findings that we report here is limited in its ability to generalise across different contexts and problems. Secondly, the lessons that were analysed came from teacher-frontal teaching and group presentation of solution and excluded students’ group discussions. We acknowledge that there could potentially be talk that could involve practical and scientific reasonings within group work. There are two practical consideration for choosing to analyse the first and presentation segments of the suite of lesson. Firstly, these two lessons involved participation from everyone in class and we wanted to survey the use of practical and scientific reasoning by the students as a class. Secondly, methodologically, clarity of utterances is important for accurate analysis and as students were wearing face masks during the data collection, their utterances during group discussions lack the clarity for accurate transcription and analysis. Thirdly, insights from this study were gleaned from a small sample of six classes of students. Further work could involve more classes of students although that could require more resources devoted to analysis of the videos. Finally, the number of students varied across groups and this could potentially affect the reasoning practices during discussions.

Data Availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

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The authors would like to acknowledge the contributions of the other members of the research team who gave their comment and feedback in the conceptualization stage.

This study is funded by Office of Education Research grant OER 24/19 TAL.

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Published: 20 April 2024

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

Xiao Wang 1 ,
Peisi Zhong 1 ,
Mei Liu 2 ,
Chao Zhang 1 &
Shihao Yang 1

Scientific Reports volume 14 , Article number: 9047 ( 2024 ) Cite this article

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Computer science
Mechanical engineering

This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm with a deep temporal difference network is proposed to minimize the makespan. Firstly, the FDSSP is defined as the mathematical model of the flexible job-shop scheduling problem joined to the assembly constraint level. It is translated into a Markov decision process that directly selects behavioral strategies according to historical machining state data. Secondly, the proposed ten generic state features are input into the deep neural network model to fit the state value function. Similarly, eight simple constructive heuristics are used as candidate actions for scheduling decisions. From the greedy mechanism, optimally combined actions of all machines are obtained for each decision step. Finally, a deep temporal difference reinforcement learning framework is established, and a large number of comparative experiments are designed to analyze the basic performance of this algorithm. The results showed that the proposed algorithm was better than most other methods, which contributed to solving the practical production problem of the manufacturing industry.

Introduction

With the development of artificial intelligence and big data technologies, intelligent scheduling plays a decision-making role in resource allocation and equipment management of advanced manufacturing systems 1 . Flexible job-shop scheduling problem (FJSP), covering operation research, sequencing theory, and optimization methods, is mainly to determine the processing equipment and process path planning. It is one of the hot topics in the scheduling system 2 . Especially, flexible double shop scheduling problem (FDSSP) that considers job shop and assembly shop is a kind of practical extension of FJSP 3 . Compared to FJSP where assembly-associated jobs can start only after machining is completed, FDSSP is the essential key to collaborating with the scheduling of associated jobs. It can reduce the assembly waiting time of associated jobs when they enter the assembly workshop at the same time as possible, which is conducive to prioritizing the processing of urgent jobs and improving production efficiency.

Over recent decades, FDSSP relatively less studied. It is mainly divided into two categories: non-assembly scheduling (Lu et al. 4 ; Thurer et al. 5 ) and assembly scheduling (Zou et al. 6 ). The former, not involving specific assembly constraints, is to accomplish all tasks of the job shop before assembly operation with only a certain extra assembly period. It is suitable for simple products or short assembly time relative to processing time. The latter should be considered to process and assemble all jobs at the same time. Hence, it has high complexity and practical application.

FDSSP is a comprehensive scheduling problem that offers a subtle fusion of flexible process plans with assembly operations in the two adjacent working shops. It is a hierarchical coupling-constrained optimization problem (HCC). Researchers have spent quite a considerable effort on FJSP, which can be divided into two categories: exact methods (EM) and approximation methods (AM). EM can guarantee the global optimal solution, but usually only solves small-scale problems such as mathematical programming (Zhang and Wang 7 ; Nourali et al. 8 , 9 ) and Branch and Bound (B&B) (Brucker and Schlie 10 ; Carlos Soto et al. 11 ; Özgüven 12 ). AM can get the solution to the problem quickly, but it can't guarantee the best explanation. It is very suitable for solving large-scale problems, such as genetic algorithm (GA) (Tian et al. 13 ), particle swarm optimization (PSO) (Nouiri et al. 14 ), ant colony optimization (ACO) (Huang and Yu 15 ; Zhu et al. 16 ; Zhang et al. 17 ), multi-agent system algorithm (Cheng et al. 18 , 19 ).

These scheduling algorithms are designed along the lines of “modeling, analysis, and optimization”. It cannot effectively use real-time data and historical data, which makes it difficult to deal with the complex and changeable production scheduling problem. However, reinforcement learning (RL) has the advantages of real-time and flexibility. It can directly select behavior strategies according to the input processing state. One of the earliest studies was from Riedmiller and Riedmiller 20 , who proposed a Q-learning algorithm in RL to develop the single-machine scheduling problem to minimize the summed tardiness. The agents used the common scheduling rules as the behavior of the system such as earliest due date (EDD), longest processing time (LPT), minimal slack (MS), etc. Later, some scholars have carried out remarkable in this field, followed by more detail in later sections.

In this paper, the application of DRL, namely deep temporal difference network (DTDN) to scheduling problems in the flexible double shop system is presented. The main contributions of this work are summarized below. (1) The flexible job-shop scheduling model with the assembly constraint level was proposed to redefine the flexible double-shop scheduling problem. Specifically, the jobs assembly constraint level was designed for the assembly shop. (2) We established a deep temporal difference network reinforcement learning framework that defined state space, action space, and rewards space. (3) We applied a deep neural network that inputs the proposed generic state features to fit the state value function.

Related work

Related work is reviewed under two parts: (1) the flexible double shop scheduling problem and (2) the DRL scheduling.

Flexible double shop scheduling problem

The flexible job shop scheduling problem, which was introduced by Brucker and Schlie 10 in 1990, has received widespread attention. However, the flexible double shop scheduling problem has been little studied. Nourali and Imanipour 8 firstly introduced the assembly job scheduling problem with sequence-dependent setup times. Zhang et al. 7 deeply investigated distributed particle swarm optimization to solve multi-objective optimization problems (makespan, total tardiness, and total workload). Zheng et al. applied the master-apprentice evolutionary algorithm to cope with the assembly job-shop scheduling problem with random machine breakdown and uncertain processing time. Tian et al. 13 , utilizing a genetic algorithm with variable neighborhood search, studied the distributed assembly job shop scheduling problem to minimize maximum completion time. Cheng et al. 18 established the adaptive simulated annealing algorithm to solve the mathematical model of the assembly job-shop scheduling with lot streaming. Later, they 19 discussed the spatial temporal links among three stages with differentiated lot size. Demir and Erden 21 proposed Genetic Algorithm and Ant Colony Optimization Algorithm to minimize the earliness, tardiness, and due-dates for the dynamic integrated process plan, scheduling, and due date assignment problem. Fan et al. 22 studied FJSP with lot-streaming and machine reconfigurations (FJSP-LSMR) to minimize the total weighted tardiness. To deal with the two decision steps for FJSP, namely, the job sequencing and the job routing, Zhang et al. 23 presented a new deep reinforcement learning with multi-agent graphs model. Erden et al. 24 designed an improved integer and categorical particle swarm optimization algorithm to solve the dynamic integrated process planning, scheduling, and due date assignment problem, in which the earliness, tardiness, and due dates in practical problems are fully considered. Su et al. 25 established a framework that used the graph neural network and deep reinforcement learning to solve JSP with dynamic events and uncertainty. Fontes et al. 26 utilized a hybrid particle swarm optimization and simulated annealing algorithm to deal with the JSP with transport resources. Burmeister et al. 27 , applying a multi-objective memetic algorithm with non-dominated sorting genetic algorithm, proposed an energy cost-aware FJSP model based on minimization of both makespan and energy costs. Carlucci et al. 28 presented a decision scheduling model that simultaneously handled the power constraint and the variable speed of machine tools.

DRL scheduling problem

In recent years, RL, one of the three types of machine learning, has been successfully applied in some fields such as computing resource scheduling, robot control, and elevator scheduling. Among them, many scholars focused on the production scheduling system by RL. Liu et al. 29 proposed a parallel algorithm that utilizes asynchronous updates and deep deterministic policy gradients to solve the job shop scheduling problem. Using MMDP to build this model, the state space is represented in the JSSP environment by the processing time matrix, allocation matrix and activation matrix. And, action spaces are denoted by simple scheduling rules. Wei and Zhao 30 suggested the conception of the production pressure and the job’s estimated mean lateness for respectively defining the system feature and the policy of reward or penalty. The Q-learning algorithm was applied to the determination of the composite machine rules. However, this method can’t describe the actual complex machining process. Luo et al. 31 used the PPO algorithm to select processes in a discrete action space and verified its superiority in solving flexible job shop scheduling problems. However, the PPO algorithm has not been studied more thoroughly to improve its performance. Mouelhi-Chibani and Pierreval 32 proposed a neural network (NN) to dynamically select dispatching rules according to the current system status and the workshop parameters. RL can take the scheduling strategy which adapts to the actual system state. Song et al. 33 presented a method using DRL to learn priority dispatch rules (PDRs) and graph neural networks (GNNs) for FJSP. A new kind of heterogeneous graph scheduling state representation was employed to combine operation selection and machine allocation into one composite decision, which achieved high-quality learning of PDRs. Chen et al. 34 presented a rule-driven dispatching method based on the data envelopment analysis to solve the multi-objective dynamic job shop scheduling problem. An agent was trained to obtain the elementary rules with the WIP fluctuation of a machine. Shahrabi et al. 35 introduced the dynamic job shop scheduling problem (DJSSP) that considered machine breakdowns and random job arrivals. In their work, the dispatching rules were based on variable neighborhood search (VNS) and compared with some common dispatching rules and the general variable neighborhood search. Wang 36 designed an improved Q-learning with the clustering and greedy search policy. A dynamic scheduling system model with multi-agent technology was built including buffer, machine, state, and job agent to maximize the weighted mean of the fuzzy earning. Shiue et al. 37 established a procedure in which they planned the real-time scheduling knowledge base (RTSKB) using multiple dispatching rules (MDRs). Significantly, MDRs incorporated two mechanisms including an off-online learning module and a Q-learning-based RL module. So far, these algorithms have lacked a unified scheduling problem name. Che et al. 38 , applying a deep reinforcement learning based multi-objective evolutionary algorithm, proposed a multi-objective optimization model for the scheduling problem of oxygen production system. Yuan et al. 39 suggested a novel framework that translated a combined optimization problem into a multi-stage sequential decision-making problem. This framework is used a multi-agent double Deep Q-network algorithm for FJSP.

This research on the application of RL in these scheduling problems (Table 1 ) shows that RL is an effective method to solve the scheduling problem. This algorithm has the following characteristics:

RL is a decision-making algorithm directly oriented to long-term goals based on state or action value.

RL doesn’t need a complete mathematical model of the learning environment. It can imitate human experience, and learn and accumulate experience from the examples or simulation experiments that have been solved.

RL needs supervision and teaching. It adjusts the policy according to the evaluation reward obtained in the interaction process. So, it makes optimal responses to different system states.

Problem formulation

Mathematical model.

We introduce FDSSP by considering the production scheduling problem of the hydraulic cylinder. The hydraulic cylinder processes flow diagram is simplified to a production scheduling model in Fig. 1 . Each cylinder 40 , 41 is assembled from several components: body, bottom, piston, piston rod, lifting lug, O-ring, seal ring, piston pin, and wiper, as shown in Fig. 1 a. The cylinder body 3 is generally made of seamless steel pipe. Its internal machining accuracy is highly required. Piston 4 and piston rod 6 are connected using snap ring 2. The piston rod 6 is guided by guide sleeve 7 and sealed by seal ring 5. Cylinder bottom 1 and body 3 are respectively opened with oil inlet and outlet ports. When the right chamber of the hydraulic cylinder is filled with oil, the piston moves left. Inversely, the piston moves right.

Integrated production of hydraulic cylinder: ( a ) structure charts; ( b ) production layout; ( c ) job shop; ( d ) assembly shop.

The shop floor is divided into two areas, namely the job shop and the assembly shop. The product is started from the order and is finished with the assembly (Fig. 1 b). The job shop is equipped with three machines (fine turning, CNC milling, and electric spark) (Fig. 1 c). The assembly workshop has two assembly robots ( \(A_{1}\) , \(A_{2}\) ) (Fig. 1 d). Each operation can be completed by multiple alternative machines. After each operation k is completed, job j needs to enter the quality control center for quality inspection. If the quality is acceptable, a job is moved to the next operation k + 1; if instead, it is returned to the current operation to be queued and reworked again. The assembly operation is a complete kit assembly, which means that the assembly operation does not begin until the all job is completed.

Since the assembly operation may be relatively short and fixed, the planned start time of the assembly operation can be extrapolated from the delivery date of the order. In this paper, we concentrate on the job shop scheduling in a way that the completion time of each job is as close to the planned start time of the assembly as possible. The assembly shop is defined as the assembly constraint level.

Based on the above example, the FDSSP can be described as follows: supposing that there are n jobs to be processed in the job shop equipped with \(m\) machines. Each job j ( \(j \in \{ 1,2, \ldots ,N\}\) ) including \(O\) operations k ( \(k \in \{ 1,2, \ldots ,O\}\) ) needs to be processed according to the specified route. Each operation k can be selected processing on any powerful machines m ( \(m \in \{ 1,2, \ldots ,M_{ij} \}\) ) in \(M_{ij}\) machines. Meanwhile, the machine m can process different operations k of different jobs. Hence, there is a great discrepancy in the processing time of the operation \(k\) on different machines , which makes the study of scheduling algorithms particularly significant. The model parameters and indices are shown in Table 2 .

Assembly restraint level definition

The job after assembly is referred to as the constrained job, and the job before assembly is referenced as the front job. Firstly, according to the assembly constraint relationship, all jobs constraint levels that have no tight front constraint are set to 1. Jobs with undefined constraint levels make up the job set, which is denoted by U . Then, the job set \(J_{set}\) is formed from U in sequence taking out all tight front jobs \(J_{k}\) . Determining whether the constraint levels in \({J}_{set}\) have all been determined. If so, the level of the job \(J_{k}\) is set to \(\max (L(J_{set} ) + 1)\) , i.e., \(L(J_{k} ) = \max (L(J_{set} )) + 1\) . When not, it puts the job \(J_{k}\) back into U until the constraint levels of all jobs have been determined.

Other assumptions are considered as follows:

The processing times of each operation by each machine are determined and known.

Each job can select only one process path. And, one operation can only be processed by one machine at a time.

The sum of the start time and processing time of an operation is less than or equal to the makespan of the operation.

The makespan of the previous operation is less than or equal to the start time of the next operation.

Completion time of products is the sum of processing time and assembly time.

The operation of each machine is cyclic.

Intermediate conversion time of the job, transferring from the job shop to the assembly shop, is omitted.

Decision variables:

According to the literature reviewed 7 , 42 , 43 , 44 , 45 makespan is the most sufficiently studied objective. In this study, the objective of the model is as follows:

Transformation of scheduling problem

Definition of state-space.

The state features can reflect the main features of the production environment. The division of state space is the basis for the reasonable selection of scheduling rules for the system. Nevertheless, owing to the constantly changing production environment, the complete system state is continuous and often described by tens or even dozens of state characteristics on the job shop.

To describe the state space in detail, the following state features are defined:

The state features can describe the main features and changes of the scheduling environment in detail, including the global features and local features of the system.

The states of all problems are represented by a common feature set.

Different scheduling problems can be represented and summarized by state features.

State feature is a numerical representation of state variables.

The state should be easy to calculate.

To facilitate the expression with the formula, the processing state of the processes is recorded as \({P}_{jk}=\{\mathrm{0,1}\}\) , i.e., the operations are not processed is \(P_{jk} = 0\) , and has been processed is recorded as \(P_{jk} = 1\) . The operations to be processed on the machine are arranged in descending order of time length, and the resulting process sequence is denoted as \(list(m) = \{ J_{m1} ,J_{m2} ,...,J_{{mv_{m} }} \}\) , where \(v_{m}\) is the number of processes to be processed on machine m . As shown in Table 3 , we define ten state features of the shop environment.

Definition of action space

Panwalker and Iskander 46 , summarizing the previous studies, elaborated 113 different combinations of dispatching rules. These rules defined the useful types of problems and measures of performance. The SCH is chosen to define a candidate set of behaviors for each machine, where priority assignment rules for reinforcement learning can overcome short-sighted natures. Behaviors that are relevant or irrelevant to the conversion should be adopted to take full advantage of existing scheduling theory and the ability of the intelligence to learn from it. In Table 4 , eight common behaviors are selected as candidate sets.

Definition of rewards

The definition of the reward function is closely related to the objective function. The agent is rewarded according to the result of the change of the system state after the implementation of the synthetic behavior and the reward function. The reward function is chosen to be defined according to the following rules.

The immediate reward for each state transition reflects the immediate effect of the action performed, which results in a short-term impact on the scheduling plan.

The cumulative total reward result reflects the long-term outcome of the execution strategy, denoted as the optimal value of the objective function.

This reward function can be applied to scheduling problems of different sizes.

The literature 47 shows a direct relationship between \({C}_{max}\) and machine utilization (e.g., minimizing the makespan is equal to maximum machine utilization). This study is devoted to addressing minimizing the makespan. The immediate reward earned for each state transition reflects the immediate impact of the action performed. It also represents the short-term impact of the action on the scheduling scheme. Cumulative rewards reflect the long-term effects, which is the goal of RL maximization.

where \(U_{ave} (t)\) is the average machine utilization rate. Let \(C_{\max } (t)\) denotes the completion time of the last operation assigned on machine m at scheduling point t . \(O_{t}\) is the current number of operations for the job i that have been assigned. Define the machine m utilization rate as \(U_{k} (t)\) , which can be calculated by \(U_{k} (t) = \frac{{\sum\nolimits_{1}^{n} {\sum\nolimits_{k = 1}^{{O_{i} (t)}} {t_{jkm} x_{jkm} } } }}{{C_{\max } (t)}}\) . Let \(r_{k} = U_{k} (t) - U_{k - 1} (t)\) , then the cumulative reward R can be calculated as follows:

where k is the counter for the allocation operation. It can be considered as a discrete-time step in RL. \(P = \sum\nolimits_{1}^{n} {\sum\nolimits_{k = 1}^{{o_{i} (t)}} {t_{jkm} x_{jkm} } }\) . \(U_{k} (t)\) and \(C_{\max } (t)\) are machine utilization and makespan at time step k .

Proposed methods for the FDSSP

Related work of rl.

RL is a specific class of machine learning (ML) problems that can achieve global optimality 48 , 49 , 50 . In an RL model 51 , the decision-maker chooses an appropriate action by observing the environment and is rewarded for doing so. RL algorithms needn’t know many states and the state transfer probability matrix during iterations. RL is transformed into the model of solving the optimal solution of Markov decision models, which is mainly used to solve sequential decision problems. The most important feature of RL is that there is no correct answer in the learning process, rather learning is done through reward signals.

Procedure of TD with evaluating state value

Markov decision processes (MDP)

Markov is the property that the next state \(s_{t + 1}\) in an RL system is related only to the current state \(s_{t}\) . The Markov decision process is described by 5-tuple as follows:

where S is a finite set of states, characterizing the description of the environmental state; A is a finite set of action spaces, representing the set of behaviors that can be taken; P is a state transfer rate function; R is a reward function; \(\gamma\) is a discount factor.

The objective of RL is to enable the agent to find an optimal strategy \(\pi^{ * }\) through continuous experimentation in the environment that maximizes the expected cumulative reward function obtained by following the strategy from any state. The reward function is determined by further defining the value function. The state-value function \(v_{\pi } (s)\) and the action-value function \(q_{\pi } (s)\) under the strategy are defined as follows.

Updating Bellman's expectation equation with the optimal strategy yields the optimal equation as follows:

Temporal difference algorithm

The TD algorithm 52 , combining Monte Carlo and dynamic planning methods, uses the classical Bellman formula to iterate until the value function converges. The basic iteration formula is as follows:

where \(r_{t + 1} { + }\gamma V(s_{t + 1} )\) is the objective of TD; \(r_{t + 1} { + }\gamma V(s_{t + 1} ) - V(s_{t} )\) is the deviation of TD; \(\alpha\) is the learning rate. The procedure to calculate \(v(s)\) is given in Algorithm 1.

Deep learning model

Deep neural network.

Deep learning 53 is a type of representation learning that is based on artificial neural networks. Deep neural network structures have greater capacity and exponential representation space, which makes it easier to learn and represent a variety of features with a significantly reduced number of neurons.

The recent success of deep learning relies heavily on massive amounts of training data, flexible models, sufficient computing power, and prior experience to fight against dimensional disasters. Hinton 54 has proposed a technique combining pre-training and fine-tuning to drastically reduce the time training a multi-layer neural network. Various optimization techniques have emerged to further alleviate the gradient disappearance problem. In particular, an application of a technique known as “Deep Residuals” 55 can enable more than a hundred network layers.Algorithm 1

Procedure of DTDN

Activation function

The activation function, a central unit in the design of neural networks, gives the ability to learn and adapt for the neurons 56 , 57 . It incorporates nonlinear factors in the neural network to address the defect of expression ability of the linear model. If the activation function isn’t used, the output of each layer is a linear function of the inputs of the previous layer. No matter how many layers the neural network has, the output is a linear combination of the inputs. Common activation functions include step functions, Sigmoid functions, Tanh functions, and approximate biological neuronal activation functions such as Relu, Leaky-Relu, and Softplus. Because approximate biological neuronal activation functions are better than traditional functions in most network applications, Relu is used in this paper.

Optimization function

Optimization function 58 , one of the core problems in neural network training, not only speeds up the solution process but also reduces the influence of hyperparameters on the solution process. Common optimization algorithms used in research applications are the stochastic gradient descent algorithm (SGD), adaptive gradient algorithm (AdaGrad), root mean square prop algorithm (RMSProp), and Adam algorithm.

In this paper, the deep neural network is made up of seven connection layers, which contain one input layer, five implicit layers, and one output layer. Figure 2 b gives the structure of the neural network.

DTDN algorithm running model (3 × 3): Deep neural network model of state perception in agent: ( a ) Deep neural network model of state perception; ( b ) Deep neural network structure.

Exploration and exploitation

FDSSP can be classified as a multi-stage decision-making problem with terminals. To balance the allocation of exploration and exploitation of the agent in environmental interactions, a greedy strategy \(\varepsilon\) is used as a strategy for selecting behavior. Greedy strategy is the selection of a greedy behavior with probability \(1 - \varepsilon\) ( \(0 < \varepsilon < 1\) ) and the random selection of any optional behavior with probability \(\varepsilon\) , where \(\varepsilon\) is the exploration factor. Suppose \(P(s,a)\) denotes the probability of selecting a behavior at the decision state. The expression is as follows:

where \(A(s)\) is the set of combinatorial behaviors that are candidates in the state \(s\) ; \(|A\left(s\right)|\) is the number of behaviors that can be chosen in the state \(s\) ; \({a}^{*}(s)\) is the greedy behavior of the state. It denotes Eq. ( 7 ) as follows:

where \({r}_{ss{\prime}}^{a}\) is the immediate reward that takes a combination of actions from state \(s\) to state \({s}^{,}\) .

Deep temporal difference network model

To briefly describe the implementation process, a workshop visualization ( \(m = 3,n = 3\) ) is proposed in Fig. 2 . The hexagonal shape represents the jobs. Hexahedra represents waiting for queues of sufficient capacity.

At the start of processing, the scheduling system is in the initial state \({S}_{0}\) , i.e., all jobs are in the first waiting queue \(Q_{1}\) with all machines free. Then the first machine selects an action \(a(k)\) ( \(1 < k < 8\) ). A job in the queue \(Q_{1}\) is selected for processing while other machines select the action \(a(8)\) . Whenever any machines complete an operation, the system moves to a new state \(S_{t}\) . In this state, each machine selects an action to perform. When another operation is completed, the system moves to a new state \(S_{{t{ + }1}}\) , which gives the agent one reward \(r_{t + 1}\) . \(r_{t + 1}\) can be calculated by the time interval between the two states. Since at each decision moment, each machine simultaneously selects one act to execute. In actuality, the system implements a multidimensional behavior with a combination of m sub-activities at a time in the state \(S_{t} (a_{t + 1} = (a_{1} ,a_{2} ,...,a_{m} ))\) . When the system reaches the termination state \(S_{T}\) , it means that all queues are empty and that all jobs have been processed. Hence, a scheduling plan is obtained.

The deep Q-network (DQN) output layer uses several nodes to represent a finite number of discrete action values. However, it cannot cover the exponential multidimensional action space. When the Q-learning online evaluates action values for heterogeneous strategies, it results in over-estimation that optimal value replaces actual interaction values. Hence the method is not directly applied to the multi-dimensional action space problem. Temporal difference learning with the same strategy, state-values indirectly calculating action-values, is proposed to replace Q learning and state values are indirectly calculated for behavior values, which is suitable for selecting multi-dimensional action in Algorithm 2.

Experiment study

To evaluate the validity of the proposed algorithm, the experiments have been conducted utilizing different test cases in four parts. First of all, according to the standard test set established in Kacem 59 , we use eight small-scale cases to compare with other algorithms 17 , 32 , 60 , 61 in “ Small scale FDSSP " section. Then, in “ Comparisons with the proposed dispatching rules " section, we compare the proposed DTDN algorithm with the Q-Learning (QL) algorithm (Jiménez 62 ) and deep deterministic policy gradient (DDPG) algorithm (Liu 63 ) on different performances in Brandimarte 64 . Moreover, for large-scale instances, we designed our test cases, which included 30 FDSSP problems of varying complexity, as shown in Section " Large-scale instances of FDSSP ". Last but not least, in " Case Study: production scheduling problem " section, we illustrate in detail the application of our algorithm in a case study of solving the hydraulic cylinder production scheduling problem.

The DTDN algorithm is coded in Python 3.7 language on JetBrains PyCharm Community Edition 2019.2.1 × 64 and runs on Intel Core i9-10900x @ 3.7GHz CPU and 16 GB RAM. First of all, we build FDSSP environment classes, machine classes, and job classes in an object-oriented manner on the RL platform OpenAI Gym. Gym specifies the main member methods of environment classes as a framework, including init, reset, step, render, and close. Then, an agent that executes the algorithm iterates interactively with the environment. The deep neural network model of the agent is implemented with the back-end TensorFlow. The experimental data are shown in Table 5 .

The selection of parameters may affect the quality of the solution, thus general principles can be followed. The discount factor \(\gamma\) measures the weight of the subsequent state value on the total return, which is why it generally takes a value close to 1 (i.e., \(\gamma = 0.95\) ). To facilitate full exploration of the strategy space during the initial phase of the iteration, the \(\varepsilon\) -greedy strategy sets the initial value of \(\varepsilon = 1\) and decays with the discount rate of 0.995. Set the learning rate: \(\alpha = 5 \times 10^{ - 4}\) ; the maximum number of interactions: \(MAX - EPISODE = 800\) ; memory D capacity: \(N = 6000\) ; and sample batch: \(BATCH - SIZE = 64\) . The deep neural network of the agent is shown in Fig. 2 b, in which the network parameters adopt a random initialization strategy.

Performance metrics: The relative percentage deviation (RPD) and average relative percentage deviation (ARPD) are described as follows:

where \(C_{\max }\) are the optimal results of algorithms; \(LB\) is the optimal results of the Branch and Bound algorithm. It represents the most ideal solution result and is not possible to achieve.

Small scale FDSSP

To prove the validity of the solution process in this study, the cases proposed by Kacem. are validated. Where, the number of jobs ( n ), the number of machines ( m ), and each operation of jobs ( \(O_{ij}\) ) are represented. For example, \(n \times m\) is a case of a set consisting of jobs and machines. The literature with the same case study as this paper is selected for comparison [Zhang 17 (DACS); Xing et al. 60 (SM); Moslehi 32 (PSO); Li et al. 61 (HTSA)], which ensures the credibility of the comparison results. Meanwhile, each case is run ten times to obtain the combined optimal solution. CPU times of various algorithms are calculated by the “relative ratio” downloaded from https://www.cpubenchmark.net/ (Table 6 ). The results show that the optimal solution of DTDN and other algorithms are the same as LB, but CPU running time is very significantly different for small-scale problems (Kacem) in Table 7 .

As designed in Table 8 a, it can be seen that the algorithm progressively generates an optimal production schedule (35). An optimal policy set \(\{ \pi^{ * } \} = \{ (6,8,8,8),(2,1,8,8), \ldots ,(8,8,8,1)\}\) is the operation sequence of each job on the machine. Where the number of parentheses in the policy set indicates the combined behavior of the four machines consisting of the behavior number taken in the corresponding state. Where the number in parentheses in the policy set indicates that the four machines in the corresponding state consisting of the behavior number adopt the combined action. At each decision time point, since most of the machine waiting queues are empty or in-process, their feasible action space includes only \(a(8)\) , which saves computation time. Moreover, the comparison of test results for Problem 2 (Table 8 b) shows that the optimal solution of the DTDN algorithm (426) is improved by 4.3% and 2.7% compared to the Nawaz Encore Han (NEH) (445) algorithms and NEH-KK algorithms (438), respectively.

Comparisons with the proposed dispatching rules

To verify the efficiency and generality of the proposed DTDN, we planned the Brandimarte 64 data set as our adopted data set. Scores and RPD of the seven dispatching rules on each data case are tallied. As known from Table 9 , the proposed DTDN algorithm compared with other algorithms can obtain better solutions, and some of them are already below the upper bound of the original cases. The actions that are used more than 10% are FIFO, SPT, LPT, SRRT, LRPT, MOR, and EDD in Fig. 3 . It is known that these actions have a greater contribution to obtaining the optimal solution and thus have a greater utilization value. The frequency distribution of other actions was relatively even, but the performance was not obvious. Therefore, it can be considered to add other heuristic behaviors to the candidate action space, which eliminates some underutilized behaviors to streamline the actions.

Performance comparison of action space (dispatching rules) under different data cases.

Large-scale instances of FDSSP

In this study, to study the performance of DTDN on large-scale problems, the results of Brandimarte cases are further compared with problem sizes ranging from BC, DP, and BR data cases in a total of 30 data cases. The solution results of the proposed DTDN are compared with Gao et al. 65 (HGA), Mastrolilli and Gambardella 66 (MG), Sun et al. 67 (HMEA), Chen et al.69 (SLGA), and Reddy 68 [teaching–learning based optimization (TLBO)], which are shown in Table 10 and Fig. 4 . The test results show that the proposed DTDN algorithm can find better computational results globally through a large amount of trial and error in the solution procedure. The obtained performance index results are better than traditional optimization methods for different scales of arithmetic cases, demonstrating the validity of the DTDN algorithm for FDSSP.

Box plots based on the results in Tables 10 and 11 .

Case study: production scheduling problem

Nourali 8 , 9 proposed a useful benchmark of FDSSP, including 40 different data cases. The solution results of the proposed DTDN are compared with Huang et al 69 [particle swarm optimization (PSO)]; Zhang and Wong 7 (constraint programming (CP)), and Zhang et al. 17 [distributed ant colony system (DACS)]. The results are displayed in Table 11 and Fig. 4 , and the following conclusions can be summarized. The optimal solutions of this algorithm are all within [LB, UB], indicating that the solutions are valid. The performance of DTDN is close to that of the other three algorithms. The run time from CPU Time is about as long as the other algorithms for small-scale cases, but the large-scale problems are much more efficient than the other method. Lastly, in general, this algorithm is slightly less capable of solving large-scale problems because of the large scheduling state space for large-scale problems, the large learning error using the same network structure, and the need for more iterations and a more optimized network structure to reduce the training error.

The main contribution of this paper is to propose an efficient DTDN method for FDSSP in a flexible shop production environment to minimize makespan. The Q learning in the deep reinforcement learning algorithm DQN is transformed into the temporal differential TD learning with state value. Hence, the deep temporal differential reinforcement learning algorithm is obtained, which is successfully applied to the shop scheduling problem. As shown by experiments, the algorithm can obtain a better solution in a smaller number of iterations compared to simply constructed heuristic or population intelligence algorithms. Because of the introduction of state features, heuristic behaviors, and deep neural networks, the algorithm is highly flexible and dynamic. The advantages of the proposed algorithm include as following:

The algorithm can learn and real-time. Since the selection from the input state to the neural network is made by the SCH Algorithm with basic rules. When the neural network is successfully trained, the previous empirical patterns are stored in the network parameters that can make scheduling decisions in real time.

The algorithm model is more flexible. The state features, behavior rules, and neural network size can be flexibly modified as needed. The constructive process is closer to the actual scheduling, which is not only applicable to NPFS problems with greater computational complexity but also suitable for solving dynamic scheduling problems from the principle.

Limitations and future work

Due to the shortcomings of the study, further work can be considered in the following aspects.

Scheduling model. Significantly, RL can add and subtract state features to better describe the processing state with minimal redundancy. Searching for more efficient and practical heuristic behaviors can fit and generalize stronger value function generalizer structures. What's more, adding or subtracting candidate behavior sets can be considered to add more highly utilized constructive heuristic behaviors.

Algorithm procedure. The DTDN algorithm itself has been proposed after many types of improvements. For example, the DTDN algorithm with priority playback memory for memory sampling priority can improve the efficiency of algorithm iteration.

Algorithm application. There is a large development space for the algorithm with the continuous progress of deep neural network theory and the increasing computer computing power. The algorithm can be extended to apply to more complex job shop scheduling problems and other dynamic scheduling problems.

Data availability

All data mentioned in the paper are available through Xiao Wang. Email: [email protected].

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This research was funded by the Supported by the Natural Science Foundation of Shandong Province (No. ZR202103070107) and the National Natural Science Foundation of China (52234005).

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Conceptualization, X.W.; Methodology, X.W and M.L.; Software, X.W and C.Z.; Validation, M.L. and P.Z.; formal analysis, X.W.; data curation, X.W.; Resources, C.Z.; visualization, S.Y. All authors have reviewed and agreed to the published version of the manuscript.

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Wang, X., Zhong, P., Liu, M. et al. A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem. Sci Rep 14 , 9047 (2024). https://doi.org/10.1038/s41598-024-59414-8

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Jeff Heyck-Williams (He, His, Him)

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NGLC Invites Applications from New England High School Teams for Our Fall 2024 Learning Excursion

Bring Your Vision for Student Success to Life with NGLC and Bravely

How to Nurture Diverse and Inclusive Classrooms through Play

Problem-Based Learning

What is Problem-Based Learning

Problem-based learning builds agency & independence

Problem-based learning promotes adaptability & flexibility

Problem-based learning is persistent

Problem-based learning is civically engaged

Make it Real

Make it Relevant

Field Studies

Make an Impact

Independence

Adapting Communication to Different Audiences and Purposes

Comprehending and Critiquing

Valuing Evidence

Strategic Use of Digital Media

Language Arts

Reading Standards

Writing Standards

Speaking & Listening Standards

Mathematics

Plan Your PBL Experience

Start Small – Focus on Practices as Entry Points

Be Honest About Project Constraints

Identify Intersections With Your Curriculum

Turn Limitations Into Opportunities

Take Risks and Model Perseverance

Be Less Helpful

Be Flexible

Experts are Everywhere

Maintain a List of Your Support Networks

Encourage Original Research

Portfolio-based Assessment

Feedback-based Assessment

A Word From Verywell