research paper on online education during covid 19

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• Published: 25 January 2021

Online education in the post-COVID era

• Barbara B. Lockee 1  

Nature Electronics volume  4 ,  pages 5–6 ( 2021 ) Cite this article

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The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

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Lockee, B.B. Online education in the post-COVID era. Nat Electron 4 , 5–6 (2021). https://doi.org/10.1038/s41928-020-00534-0

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ORIGINAL RESEARCH article

Engagement in online learning: student attitudes and behavior during covid-19.

• 1 Department of Mathematics, University of California, San Diego, San Diego, CA, United States
• 2 Halıcıo ǧ lu Data Science Institute, University of California, San Diego, San Diego, CA, United States
• 3 Joint Doctoral Program in Math and Science Education, San Diego State University, San Diego, CA, United States
• 4 Joint Doctoral Program in Math and Science Education, University of California, San Diego, San Diego, CA, United States
• 5 Department of Physical Therapy, Movement and Rehabilitation Science, Bouvé College of Health, Northeastern University, Boston, MA, United States
• 6 Art and Design, College of Arts, Media and Design, Northeastern University, Boston, MA, United States
• 7 Qualcomm Institute, University of California, San Diego, San Diego, CA, United States

The COVID-19 pandemic resulted in nearly all universities switching courses to online formats. We surveyed the online learning experience of undergraduate students ( n = 187) at a large, public research institution in course structure, interpersonal interaction, and academic resources. Data was also collected from course evaluations. Students reported decreases in live lecture engagement and attendance, with 72 percent reporting that low engagement during lectures hurt their online learning experience. A majority of students reported that they struggled with staying connected to their peers and instructors and managing the pace of coursework. Students had positive impressions, however, of their instructional staff. Majorities of students felt more comfortable asking and answering questions in online classes, suggesting that there might be features of learning online to which students are receptive, and which may also benefit in-person classes.

Introduction

In Spring 2020, 90% of higher education institutions in the United States canceled in-person instruction and shifted to emergency remote teaching (ERT) due to the COVID-19 pandemic ( Lederman, 2020 ). ERT in response to COVID-19 is qualitatively different from typical online learning instruction as students did not self-select to participate in ERT and teachers were expected to transition to online learning in an unrealistic time frame ( Brooks et al., 2020 ; Hodges et al., 2020 ; Johnson et al., 2020 ). This abrupt transition left both faculty and students without proper preparation for continuing higher education in an online environment.

In a random sample of 1,008 undergraduates who began their Spring 2020 courses in-person and ended them online, 51% of respondents said they were very satisfied with their course before the pandemic, and only 19% were very satisfied after the transition to online learning ( Means and Neisler, 2020 ). Additionally, 57% of respondents said that maintaining interest in the course material was “worse online,” 65% claimed they had fewer opportunities to collaborate with peers, and 42% said that keeping motivated was a problem ( Means and Neisler, 2020 ). Another survey of 3,089 North American higher education students had similar results with 78% of respondents saying online experiences were not engaging and 75% saying they missed face-to-face interactions with instructors and peers ( Read, 2020 ). Lastly, of the 97 university presidents surveyed in the United States by Inside Higher Ed , 81% claimed that maintaining student engagement would be challenging when moving classes online due to COVID-19 ( Inside Higher Ed, 2020 ).

In this report, we consider the measures and strategies that were implemented to engage students in online lectures at UCSD during ERT due to the COVID-19 pandemic. We investigate student perceptions of these measures and place our findings in the larger context of returning to in-person instruction and improving engagement in both online and in-person learning for undergraduates. Before diving into the current study, we first define what we mean by engagement.

Theoretical Framework and Literature Review

Student engagement.

Student engagement has three widely accepted dimensions: behavioral, cognitive and affective ( Chapman, 2002 ; Fredricks et al., 2004 , 2016 ; Mandernach, 2015 ). Each dimension has indicators ( Fredricks et al., 2004 ), or facets ( Coates, 2007 ), that manifest each dimension. Behavioral engagement refers to active responses to learning activities and is indicated by participation, persistence, and/or positive conduct. Cognitive engagement includes mental effort in learning activities and is indicated by deep learning, self-regulation, and understanding. Affective engagement is the emotional investment in learning activities and is indicated by positive reactions to the learning environment, peers, and teachers as well as a sense of belonging. A list of indicators for each dimension can be found in Bond et al. (2020) .

The literature also theorizes different influences for each engagement dimension. Most influencing factors are sociocultural in nature and can include the political, social, and teaching environment as well as relationships within the classroom ( Kahu, 2013 ). In particular, social engagement with peers and instructors creates a sense of community, which is often correlated with more effective learning outcomes ( Rovai and Wighting, 2005 ; Liu et al., 2007 ; Lear et al., 2010 ; Kendricks, 2011 ; Redmond et al., 2018 ; Chatterjee and Correia, 2020 ). Three key classroom interactions are often investigated when trying to understand the factors influencing student engagement: student-student interactions, student-instructor interactions, and student-content interactions ( Moore, 1993 ).

Student-student interactions prevent boredom and isolation by creating a dynamic sense of community ( Martin and Bolliger, 2018 ). Features that foster student-student interactions in online learning environments include group activities, peer assessment, and use of virtual communication spaces such as social media, chat forums, and discussion boards ( Revere and Kovach, 2011 ; Tess, 2013 ; Banna et al., 2015 ). In the absence of face-to-face communication, these virtual communication spaces help build student relationships ( Nicholson, 2002 ; Harrell, 2008 ). In a survey of 1,406 university students in asynchronous online courses, the students claimed to have greater satisfaction and to have learned more when more of the course grade was based on discussions, likely because discussions fostered increased student-student and student-instructor interactions ( Shea et al., 2001 ). Interestingly, in another study, graduate students in online courses claimed that student-student interactions were the least important of the three for maintaining student engagement, but that they were more likely to be engaged if an online course had online communication tools, ice breakers, and group activities ( Martin and Bolliger, 2018 ).

In the Martin and Bolliger (2018) study, the graduate students enrolled in online courses found student-instructor interactions to be the most important of the three interaction types, which supports prior work that found students perceive student-instructor interactions as more important than peer interactions in fostering engagement ( Swan and Shih, 2005 ). Student-instructor interactions increased in frequency in online classes when the following practices were implemented (1) multiple open communication channels between students and instructors ( Gaytan and McEwen, 2007 ; Dixson, 2010 ; Martin and Bolliger, 2018 ), (2) regular communication of announcements, reminders, grading rubrics, and expectations by instructors ( Martin and Bolliger, 2018 ), (3) timely and consistent feedback provided to students ( Gaytan and McEwen, 2007 ; Dixson, 2010 ; Chakraborty and Nafukho, 2014 ; Martin and Bolliger, 2018 ), and (4) instructors taking a minimal role in course discussions ( Mandernach et al., 2006 ; Dixson, 2010 ).

Student-content interactions include any interaction the student has with course content. Qualities that have been shown to increase student engagement with course content include the use of curricular materials and classroom activities that incorporate realistic scenarios, prompts that scaffold deep reflection and understanding, multimedia instructional materials, and those that allow student agency in choice of content or activity format ( Abrami et al., 2012 ; Wimpenny and Savin-Baden, 2013 ; Britt et al., 2015 ; Martin and Bolliger, 2018 ). In online learning, students need to be able to use various technologies in order to be able to engage in student-content interactions, so technical barriers such as lack of access to devices or reliable internet can be a substantial issue that deprives educational opportunities especially for students from lower socioeconomic households ( Means and Neisler, 2020 ; Reich et al., 2020 ; UNESCO, 2020 ).

Engagement in Online Learning

Bond and Bedenlier (2019) present a theoretical framework for engagement in online learning that combines the three dimensions of engagement, types of interactions that can influence the engagement dimensions, and possible short term and long term outcomes. The types of interactions are based on components present in the student’s immediate surrounding or microsystem, and are largely based on Moore’s three types of interactions: teachers, peers, and curriculum. However, the authors add technology and the classroom environment as influential components because they are particularly important for online learning.

Specific characteristics of each microsystem component can differentially modulate student engagement, and each component has at least one characteristic that specifically focuses on technology. Teacher presence, feedback, support, time invested, content expertise, information and communications technology skills and knowledge, technology acceptance, and use of technology all can influence the types of interactions students might have with their teachers which would then impact their engagement ( Zhu, 2006 ; Beer et al., 2010 ; Zepke and Leach, 2010 ; Ma et al., 2015 ; Quin, 2017 ). For curriculum/activities, the quality, design, difficulty, relevance, level of required collaboration, and use of technology can influence the types of interactions a student might encounter that could impact their engagement ( Zhu, 2006 ; Coates, 2007 ; Zepke and Leach, 2010 ; Bundick et al., 2014 ; Almarghani and Mijatovic, 2017 ; Xiao, 2017 ). Characteristics that can change the quantity and quality of peer interactions and thereby influence engagement include the amount of opportunities to collaborate, formation of respectful relationships, clear boundaries and expectations, being able to physically see each other, and sharing work with others and in turn respond to the work of others ( Nelson Laird and Kuh, 2005 ; Zhu, 2006 ; Yildiz, 2009 ; Zepke and Leach, 2010 ). When describing influential characteristics, the authors combine classroom environment and technology because in online learning, the classroom environment inherently utilizes technology. The influential characteristics of these two components are access to technology, support in using and understanding technology, usability, design, technology choice, sense of community, and types of assessment measures. All of these characteristics demonstrably influenced engagement levels in prior literature ( Zhu, 2006 ; Dixson, 2010 ; Cakir, 2013 ; Levin et al., 2013 ; Martin and Bolliger, 2018 ; Northey et al., 2018 ; Sumuer, 2018 ).

Online learning can take place in different formats, including fully synchronous, fully asynchronous, or blended ( Fadde and Vu, 2014 ). Each of these formats offers different challenges and opportunities for technological ease, time management, community, and pacing. Fully asynchronous learning is time efficient, but offers less opportunity for interactions that naturally take place in person ( Fadde and Vu, 2014 ). Instructors and students may feel underwhelmed by the lack of immediate feedback that can happen in face to face class time ( Fadde and Vu, 2014 ). Synchronous online learning is less flexible for teachers and students and requires reliable technology, but allows for more real time engagement and feedback ( Fadde and Vu, 2014 ). In blended learning courses, instructors have to coordinate and organize both the online and in person meetings and lessons, which is not as time efficient. Blended learning means there is some in person engagement which provides spontaneity and more natural personal relations ( Fadde and Vu, 2014 ). In all online formats, students may feel isolated and instructors and students need to spend more time and intention into building community ( Fadde and Vu, 2014 ; Gillett-Swan, 2017 ). Often, instructors can use learning management systems and discussion boards to help facilitate student interaction and connection ( Fadde and Vu, 2014 ). In terms of group work, engagement and participation is dependent not only on the modality of learning, but also the instructors expectations for assessment ( Gillett-Swan, 2017 ). Given the flexibility and power of online meeting and work environments, collaborating synchronously or asynchronously are both possible and effective ( Gillett-Swan, 2017 ). In online learning courses, especially fully asynchronous, students are more accountable for their learning, which may be challenging for students who struggle with self-regulating their work pace ( Gillett-Swan, 2017 ). Learning from home also means there are more distractions than when students attend class on campus. At any point during class, children, pets, or work can interrupt a student’s, or instructor’s, remote learning or teaching ( Fadde and Vu, 2014 ).

According to Raes et al. (2019) , the flexibility of a blended -or hybrid- learning environment encourages more students to show up to class when they otherwise would have taken a sick day, or would not have been able to attend due to home demands. It also equalizes learning opportunities for underrepresented groups, and more comprehensive support with two modes of interaction. On the other hand, hybrid learning can cause more strain on the instructor who may have to adapt their teaching designs for the demands of this unique format while maintaining the same standards ( Bülow, 2022 ). Due to the nature of class, some students can feel more distant to the instructor and to each other, and in many cases active class participation was difficult in hybrid learning environments. Although Bulow’s review (2022) focused on the challenges and opportunities of designing effective hybrid learning environments for the teacher, it follows that students participating in different environments will also need to adapt to foster effective active participation environments that encompass both local and remote learners.

Engagement in Emergency Remote Instruction During COVID-19

There is currently a thin literature on student perceptions of the efficacy of ERT strategies and formats in engaging students during COVID-19. Indeed, student perceptions about online learning do not indicate actual learning. This study considers student perceptions for the purpose of gathering information about what conditions help or hinder students’ comfort with engaging in online classes toward the goal of designing improved online learning opportunities in the future. The large scale surveys of undergraduate students had some items relating to engagement, but these surveys aimed to generally understand the student experience during the transition to COVID-19 induced ERT ( Means and Neisler, 2020 ; Read, 2020 ). A few small studies have surveyed or interviewed students from a single course on their perceptions of the changes made to courses to accommodate ERT ( Senn and Wessner, 2021 ), the positives and negatives of ERT ( Hussein et al., 2020 ), or the changes in their participation patterns and the course structures and instructor strategies that increase or decrease engagement in ERT ( Perets et al., 2020 ). In their survey of 73 students across the United States, Wester and colleagues specifically focused on changes to students’ cognitive, affective, and behavioral engagement due to COVID-19 induced ERT, but they did not inquire as to what were the key influencing factors for these changes. Walker and Koralesky (2021) and Shin and Hickey (2021) surveyed students from a single institution but from multiple courses and thus are most relevant to the current study. These studies aimed to understand the students’ perceptions of their engagement and influencing factors of engagement at a single institution, but they did not assess how often these factors were implemented at that institution.

The current study investigates the engagement strategies used in a large, public, research institution, students’ opinions about these course methods, and students’ overall perception of learning in-person versus during ERT. This study aims to answer the following questions:

1. How has the change from in-person to online learning affected student attendance, performance expectations of students, and participation in lectures?

2. What engagement tools are being utilized in lectures and what do students think about them?

3. What influence do social interactions with peers, teachers, and administration have on student engagement?

These three questions encompass the three different dimensions of engagement, including multiple facets of each, as well as explicitly highlighting the role of technology in student engagement.

Materials and Methods

Data were collected from two main sources: a survey of undergraduates, and Course and Professor Evaluations (CAPE). The study was deemed exempt from further review by the institution’s Institutional Review Board because identifying information was not collected.

The survey consisted of 50 questions, including demographic information as well as questions about both in-person and online learning (Refer to full survey in Supplementary Material.). The survey, hosted on Qualtrics, was distributed to undergraduate students using various social media channels, such as Reddit, Discord, and Facebook, in addition to being advertised in some courses. In total, the survey was answered by 237 students, of which 187 completed the survey in full, between January 26th and February 15, 2021. It was made clear to students that the data collected would be anonymous and used to assess engagement over the course of Fall 2019 to Fall 2020. The majority of the survey was administered using five-point Likert scales of agreement, frequency, and approval. The survey was divided into blocks, each of which used the same Likert scale. Quantitative analysis of the survey data was conducted using R, and visualized with the likert R package ( Bryer and Speerschneider, 2016 ).

A number of steps were taken to ensure that survey responses were valid. Before survey distribution, 2 cognitive interviews were conducted with undergraduate students attending the institution in order to refine the intelligibility of survey items ( Desmione and Carlson Le Floch, 2004 ). Forty-eight incomplete surveys were excluded. In addition, engagement tests were placed within the larger blocks of the survey in order to prevent respondents from clicking the same choice repeatedly without reading the prompts. The two students who answered at least one of these questions incorrectly were excluded.

Respondent Profile

Respondents were asked before the survey to confirm that they were undergraduate students attending the institution over the age of 18. Among the 187 students that filled out the survey in its entirety, 21.9% were in their first year, 28.3% in their second year, 34.2% in their third year, 11.8% in their fourth year, and 1.1% in their fifth year or beyond. It should be noted, therefore, that some students, especially first-years, had no experience with in-person college education at the institution, and these respondents were asked to indicate this for any questions about in-person learning. However, all students surveyed were asked before participating whether they had experience with online learning at the institution. 2.7% of respondents were first year transfers. 72.7% of overall respondents identified as female, 25.7% as male, 0.5% as non-binary, and 1.1% preferred not to disclose gender. In regards to ethnicity, 45.6% of respondents identified as Asian, 22.8% as White, 13.9% as Hispanic/Latinx, 1.7% as Middle Eastern, 1.6% as Black or African-American, and 2.2% as Other. 27.9% of respondents were first-generation college students, 7.7% of respondents were international students, and 9.9% of students were transfer students.

In the most recent report for the 2020–2021 academic year, the Institutional Research Department noted that out of 31,842 undergraduates, 49.8% of undergraduates are women and 49.4% are men ( University of California, San Diego Institutional Research, 2021 ). This report states that 17% of undergraduates are international students, which is a larger percentage than is represented by survey respondents ( University of California, San Diego Institutional Research, 2021 ). The institution reports 33% of undergraduates are transfer students, which are also underrepresented in the survey respondents ( University of California San Diego [UCSD], 2021b ). The ethnicity profile of the survey respondents is similar to the undergraduate student demographic at this institution. According to the institutional research report, among undergraduates, 37.1% are Asian American, 19% are White, 20.8% are Chicano/Latino, 3% are African American, 0.4% are American Indian, and 2.5% are missing data on ethnicity ( University of California, San Diego Institutional Research, 2021 ).

Course and Professor Evaluation Reviews

Data were also collected from the institution’s CAPE reviews, a university-administered survey offered prior to finals week every quarter, in which undergraduate students are asked to rate various aspects of their experience with their undergraduate courses and professors ( Courses not CAPEd for Winter 22, 2022 ). CAPE reviews are anonymous, but are sometimes incentivized by professors to increase participation.

Although it was not designed with Bond and Bedenlier’s student engagement framework in mind, the questions on the CAPE survey still address the fundamental influences on engagement established by the framework. The CAPE survey asks students how many hours a week they spend studying outside of class, the grade they expect to receive, and whether they recommend the course overall. The survey then asks questions about the professor, such as whether they explain material well, show concern for student learning, and whether the student recommends the professor overall.

In this study, we chose to look only at data from Fall 2019, a quarter where education was in-person, and Fall 2020, when courses were online. In Fall 2019, there were 65,985 total CAPE reviews submitted, out of a total of 114,258 course enrollments in classes where CAPE was made available, for a total response rate of 57.8% ( University of California San Diego [UCSD], 2021a ). The mean response rate within a class was 53.1% with a standard deviation of 20.7%. In Fall 2020, there were 65,845 CAPE responses out of a total of 118,316 possible enrollments, for a total response rate of 55.7%. The mean response rate within a class was 50.7%, with a standard deviation of 19.6%.

In order to adjust for the different course offerings between quarters, and for the different professors who might teach the same course, we selected only CAPE reviews for courses that were offered in both Fall 2019 and Fall 2020 with the same professors. This dataset contained 31,360 unique reviews (16,147 from Fall 2019 and 15,213 from Fall 2020), covering 587 class sections in Fall 2019 and 630 in Fall 2020. Since no data about the students were provided with the set, however, we do not know how many students these 31,360 reviews represent. This pairing strategy offers many interesting opportunities to compare the changes and consistencies of student reviews between both quarters in question. To keep this study focused on the three research questions and in observation of time and space limitations, analysis was only performed on the pairwise level of the general CAPE survey questions and not broken down to further granularity.

The CAPE survey was created by the designers of CAPE, not the researchers of this paper. The questions on the CAPE survey are general and only provide a partial picture of the status of student engagement in Fall 2019 and Fall 2020. The small scale survey created by this research team attempts to clarify and make meaning of the results from the CAPE data.

Data Analysis

Survey data.

Survey data was collected and exported from Qualtrics as a. csv file, then manually trimmed to include only relevant survey responses from participants who completed the survey. Data analysis was done in R using the RStudio interface, with visualizations done using the likert and ggplot2 R packages ( Bryer and Speerschneider, 2016 ; Wickham, 2016 ; R Core Team, 2020 ; RStudio Team, 2020 ). Statistical tests were performed on lecture data, using paired t -tests, and Mann–Whitney U tests of the responses; for example, when comparing attendance of in-person lectures in Fall 2019 and live online lectures on Zoom in Fall 2020.

Course and Professor Evaluation Data

As previously mentioned, analysis of CAPE reviews was restricted to courses that were offered in both Fall 2019 and 2020 with the same professor, with Fall 2019 courses being in-person and Fall 2020 courses being online. This was done since the variation of interest is the change from in-person to online education, and restricting analysis to these courses allowed the pairing of specific courses for statistical tests, as well as the adjustment for any differences in course offerings or professor choices between the two quarters. In order to compare ratings for a specific item, first, negative items were recoded if necessary. The majority of questions were on a 5-point Likert scale, though some, such as expected grade, needed conversion from categorical (A–F scale) to numerical (usually 0–4). Then, the two-sample Mann–Whitney U test was conducted on the numerical survey answers, comparing the results from Fall 2019 to those from Fall 2020. Results were then visualized using the R package ggplot2 ( Wickham, 2016 ), as well as the likert package ( Bryer and Speerschneider, 2016 ).

In this study, we aimed to take a broad look at the state of online learning at UCSD as compared to in-person learning before the COVID-19 pandemic. This assessment was split into three general categories: changes in lecture engagement and student performance, tools that professors and administrators have implemented in the face of online learning, and changes in patterns of students’ interactions with their peers and with instructors. In general, while we found that students’ ratings of their professors and course staff remained positive, there were significant decreases in lecture engagement, attendance, and perceived ability to keep up with coursework, even as expected grades rose. In addition, student-student interactions fell for the vast majority of students, which students felt hurt their learning experience.

Course and Professor Evaluation Results

How has the change from in-person to online learning affected student attendance, performance expectations, and participation in lectures, lecture attendance.

In the CAPE survey, students reported their answers to a series of questions relating to lecture attendance and engagement. Table 1 reports the results of the Mann–Whitney U test for each question, in which the results from Fall 2019 were compared to the results from Fall 2020. Statistically significant differences were found between students’ responses to the question “How often do you attend this course?” (rated on a 1–3 scale of Very Rarely, Some of the Time, and Most of the Time), although students were still most likely to report that they attended the class most of the time. Statistically significant decreases were also found for students’ agreement to the questions “Instructor is well-prepared for classes,” and “Instructor starts and finishes classes on time.” It should be noted that “attendance” was not clarified as “synchronous” or “asynchronous” attendance to survey respondents.

Table 1. Mean and standard deviations of student responses on CAPE evaluation questions relating to lecture attendance and engagement in Fall 2019 and 2020.

Expected Grades

Within the CAPE survey, students are asked, “What grade do you expect in this class?” The given options are A, B, C, D, F, Pass, and No Pass. The proportion of CAPE responses in which students reported taking the course Pass/No Pass stayed relatively constant from Fall 2019 to Fall 2020, going from 6.5% in Fall 2019 to 6.4% in Fall 2020. As can be seen in Figure 1 , participants were more likely to expect A’s in Fall 2020; in Fall 2019, the median expected grade was an A in 56.8% of classes, while in Fall 2020, this figure was 68.0%. We used a Mann–Whitney U test to test our hypothesis that there would be a difference between Fall 2019 and Fall 2020 expected grades because of students’ and instructors’ unfamiliarity with the online modality. When looking solely at classes in which students expected to receive a letter grade, after recoding letter grades to GPA equivalents, a significant difference was found between expected grades in Fall 2019 and 2020, with a mean of 3.443 in FA19 and 3.538 in FA20 ( U = 92286720, p < 0.001).

Figure 1. Distribution of grades expected by students prior to finals week in CAPE surveys in Fall 2019 and Fall 2020.

What Engagement Tools Are Being Utilized by Professors and What do Students Think About Them?

Assignments and learning.

As part of the CAPE survey, respondents were asked to rate their agreement on a 5-point Likert scale to questions about their assignments and learning experience in the class. Results are displayed in Table 2 . Statistically significant increases in student agreement, as indicated by the two-sample Mann–Whitney U test, were reported in the questions “Assignments promote learning,” “The course material is intellectually stimulating,” and “I learned a great deal from this course.”

Table 2. Student responses on CAPE evaluation statements relating to assignments, course material, and quality of learning.

What Influence do Social Interactions With Peers, Teachers, and Administration Have on Student Engagement?

Professor efficacy and accessibility.

As part of the CAPE survey, students also rated their professors in various aspects, as can be seen in Table 3 . The only significant result observed between Fall 2019 and Fall 2020 was a slight increase in student agreement with the statement “Instructor is accessible outside of class.”

Table 3. Student responses on CAPE evaluation statements relating to instructor efficacy and accessibility.

Survey Results

General satisfaction.

Respondents were asked to indicate their agreement on a 5-point Likert scale (Strongly Disagree, Disagree, Neither Agree nor Disagree, Agree, and Strongly Agree) to the statement, “In general, I am satisfied with my online learning experience at [institution].” 36% of respondents agreed with the statement, 28% neither agreed nor disagreed, and 36% disagreed.

Perceptions of Academic Performance

Students were asked to rate their agreement on a 5-point Likert scale of agreement to a series of broad questions about their online learning experience, some of which pertained to academic performance. When assessing the statement “My current online courses are more difficult than my past in-person courses,” 42% chose Strongly Agree or Agree, 32% chose Neither Agree nor Disagree, and 26% chose Disagree or Strongly Disagree. Respondents were also split on the statement “My academic performance has improved with online education,” which 28% agreed/strongly agreed with, 34% disagreed/strongly disagreed with, and 38% chose neither.

For the statement “I feel more able to manage my time effectively with online education than with in-person education,” only 34% agreed/strongly agreed with the statement while 45% disagreed/strongly disagreed and 21% chose neither. For the statement, “I feel that it is easier to deal with the pace of my course load with online education than with in-person education,” 30% of respondents agreed/strongly agreed, 54% disagreed/strongly disagreed, and 16% neither agreed nor disagreed.

Lecture Attendance by Class Type

Since the CAPE survey question regarding attendance did not specify asynchronous or synchronous attendance, students were asked on the survey created by the authors of this paper how often they attended and skipped certain types of lectures. In response to the question “During your last quarter of in-person classes, how often did you skip live, in-person lectures?,” 11% reported doing so often or always, 14% did so sometimes, and the remaining 74% did so rarely or never. The terms “Sometimes” and “Rarely” were not clarified to the respondents. This is the same scale and language used on the CAPE survey, however, which was a benefit to synthesizing and comparing this data with CAPEs. Meanwhile, for online classes, 35% reported skipping their live classes often or always, 23% did so sometimes, and 43% did so rarely or never.

Respondents were also asked about their recorded lectures, both in-person and online; while some courses at the institution are recorded and released in either audio or video form for students, most online synchronous lectures are recorded. When asked how often they watched recorded lectures instead of live lectures in-person, 12% of respondents said they did so often or always, 12% reported doing so sometimes, and 76% did so rarely or never. For online classes where recorded versions of live lectures were available, 47% of students reported watching the recorded version often or always, 21% did so sometimes, and 33% did so rarely or never.

Meanwhile, there were also some lectures during online learning that were offered only online (asynchronous), as opposed to being recorded versions of lectures that were delivered to students live over Zoom.

Students were asked questions about their lecture attendance for in-person learning pre-COVID and for online learning during the pandemic. On a 5 point Likert scale from Never to Always, 11% of students said they skipped “live, in-person lectures” in their courses pre-COVID Often or Always. On the same scale, 35% of respondents said they skipped live online lectures Often or Always. To assess the significance of these reports, we conducted a one-sided Mann–Whitney U test with the null hypothesis that the median frequency of students skipping live online lectures is greater than the median frequency of skipping live in-person lectures. Previous research suggesting that lecture attendance decreased after the COVID-19 transition motivated our alternative hypothesis that students would skip live online lectures more often ( Perets et al., 2020 ). The result was significant, meaning that this evidence suggests that students skip online lectures (Mdn = 3 “Sometimes”) more often than live in-person lectures (Mdn = 2 “Rarely”), U = 23328, p < 0.001. The results were also significant when a one-sided 2 sample t -test was performed to test if students were skipping online lectures ( M = 2.84, SD = 1.13) more often than they skipped in-person lectures ( M = 1.97, SD = 1.06), t (358.53) = 7.55, p < 0.001.

In order to clarify why students might be skipping lectures, we asked students how often they were using the recorded lecture options during in-person and online learning. 12% of respondents reported that they watched the recorded lecture “Often” or “Always” instead of attending the live lecture in-person while 47% of respondents said that they watched the recorded version of lecture, if it was offered, “Often” or “Always” rather than the live version during remote learning. When a one-sided Mann–Whitney U test was performed comparing the medians of students that utilized the recorded option during in-person classes (Mdn = 2 “Rarely”) and during online classes (Mdn = 3 “Sometimes”), the results were significant, suggesting that more students watch a recorded lecture version when it is offered during online classes, U = 6410, p < 0.001. The results are also significant with a t -test comparing the means of students that watched the recorded format during in-person classes ( M = 1.95, SD = 1.08) and during online classes ( M = 3.23, SD = 1.23), t (330.84) = –10.13, p < 0.001.

Students were asked how often they used course materials, such as a textbook or instructor provided notes and slideshows, rather than attending a live or recorded lecture to learn the necessary material. 10% of students said that they used course materials “Often” or “Always” during in-person learning while 19% of students said they used course materials “Often” or “Always” during online learning. The results were significant in a one-sided Mann–Whitney U test for the null hypothesis that the medians are equivalent for students using materials during in-person learning (Mdn = 1 “Never”) and during online learning (Mdn = 2 “Rarely”), U = 12644, p < 0.001. In other words, the evidence suggests that students use course materials instead of attending lectures more often when classes are online than when classes are in-person. A one-sided t -test also indicates that students during online learning ( M = 2.30, SD = 1.16) utilize provided materials instead of watching lecture to learn course material more often than students during in-person learning ( M = 1.76, SD = 1.03), t (364.55) = –4.72, p < 0.001.

Discussions are supplementary and sometimes mandatory classes to the lecture conducted by a teaching assistant. Students reported that during the last quarter of online classes the discussion sections tended to include synchronous live discussion instead of pre-recorded content (see Table 4 ).

Table 4. Distribution of survey responses to questions about non-mandatory discussion sections.

Reported Attendance and Engagement in Lecture

Students were asked to rate their agreement on the same 5-point Likert scale to a series of questions about their in-lecture attendance and engagement. When presented with the statement “I feel more comfortable asking questions in online classes than in in-person ones,” 56% of students agreed, 22% neither agreed nor disagreed, and 22% disagreed. Here, “agreed” includes strongly agree and disagree includes “strongly disagreed.” This was similar to the result for “I feel more comfortable answering questions in online classes than in in-person ones,” to which 56% agreed, 24% neither agreed nor disagreed, and 20% disagreed.

When students who had taken both in-person and online courses were directly asked about overall attendance of live lectures, with the statement “I attend more live lectures now that they are online than I did when lectures were in-person,” 12% agreed, 19% neither agreed nor disagreed, and 69% disagreed (with 32.5% selecting “Strongly disagree”).

Issues With Online Learning

Respondents were asked to indicate on a 5-point Likert frequency scale (Never, Rarely, Sometimes, Often, and Always) how often a series of possible issues affected their online learning. These are reported in Figure 2 . The most common technical issue was unreliable WiFi. 20% of students say unreliable WiFi happens “Often” or “Always,” 35% say this issue happens to them “Sometimes,” and 45% of students say unreliable WiFi affects their online learning “Never” or “Rarely.” The next common technological problem students face is unreliable devices. A poor physical environment affected students’ online learning for 32% of the respondents “Often” or “Always.” Issues with platforms, such as Gradescope, Canvas, and Zoom, were present but reported less often.

Figure 2. Prevalence of issues in online education among student survey respondents ( n = 187).

Course Structure

For a given possible intervention in course structure, students were asked how often their professors implemented the changes and to rate their opinion of the learning strategy. The examined changes were weekly quizzes, replacing exams with projects or other assignments, interactive polls or questions during lectures, breakout rooms within lectures, open-book or open-note exams, and optional or no-fault final exams – exams that will not count toward a student’s overall grade if their exam score does not help their grade.

Respondents’ reported frequencies of these interventions are displayed in Figure 3 , and their ratings of them are displayed in Figure 4 . In addition to being the most common intervention, open book exams were also the most popular intervention among students, with 89% of respondents reporting that they had a Good or Excellent opinion. Similarly popular were in-lecture polls, optional finals, and replacing exams with assignments, while breakout sessions had a slightly negative favorability.

Figure 3. Students’ reported frequencies of certain possible interventions in online learning.

Figure 4. Students’ reported approval of certain possible interventions in online learning.

Academic Tools and Resources

In the survey, students were asked to rate their agreement with the statement, “Online learning has made me more likely to use academic resources such as office hours, tutoring, or voluntary discussion sessions.” 42% of students agreed (includes “strongly agreed”), 23% neither agreed nor disagreed, and 35% disagreed (includes “strongly disagreed”). However, for the statement, “Difficulties accessing office hours or other academic resources have negatively interfered with my academic performance during online education,” 26% of students agreed/strongly agreed, 24% neither agreed nor disagreed, and 49% disagreed/strongly disagreed.

Respondents were asked to rate their opinion of various academic resources on a 5-point scale (Terrible, Poor, Average, Good, and Excellent) for both in-person and online classes ( Figures 5 , 6 ). The most notable change in rating was for the messaging platform Discord, which 67% of respondents saw as a Good or Excellent academic resource during online education, compared to 34% in in-person education. The learning management system Canvas also saw an increase in favorability, while favorability decreased for course discussions.

Figure 5. Students’ reported approval ratings of certain academic resources and tools when classes were in-person.

Figure 6. Students’ reported approval ratings of certain academic resources and tools when classes were online.

Respondents were asked to rate the frequency at which they and their professors turned their cameras on during lectures. 64% of students reported keeping their cameras on never or rarely, 29% reported keeping cameras on sometimes, and 6% of students reported keeping their cameras on often or always. Meanwhile, for professors, 58% of students reported that all of their professors kept their cameras on, 28% said most kept their cameras on, 9% said about half did so, and the remaining 5% said that some or none of their professors kept cameras on.

Personal Interaction

A lack of social interaction was among the largest complaints of students about online learning. 88% of respondents at least somewhat agreed with the statement “I feel less socially connected to my peers during online education than with in-person education.” When students were asked how often certain issues negatively impacted their online learning experience, 64% of respondents indicated that a lack of interaction with peers often or always impacted their learning experience, and 44% reported the same about a lack of instructor interaction.

When we asked students how they stay connected to their peers, 78.6% said that they stay connected to peers through student-run course forums, such as Discord, a messaging app that is designed to build communities of a common interest. 72.7% said they use personal communication, i.e., texting, with peers. 48.1% of students said they use faculty-run course forums, such as Piazza or Canvas. 45.5% of students surveyed keep in touch with peers through institution clubs and organizations. 29.4% of students selected that they use student-made study groups and 19.8% stay connected through their campus job.

Ratings of University Faculty and Staff

Students were asked to rate their opinion of various faculty and staff, by answering survey statements of the form “____ have been sufficiently accommodating of my academic needs and circumstances during online learning.” For instructors, 72% agreed/strongly agreed with this statement and 11% disagreed/strongly disagreed; for teaching assistants and course tutors, 81% agreed/strongly agreed, and only 2% disagreed/strongly disagreed. Meanwhile, for university administration, 39% of students agreed/strongly agreed, 34% neither agreed nor disagreed, and 26% disagreed/strongly disagreed.

Based on both the prior literature and this study, students seemed to struggle with engagement before the pandemic during in-person lectures, and it appears from the survey findings that students are struggling even more with engagement in online courses. A U.S. study investigating the teaching and learning experiences of instructors and students during the COVID-19 pandemic also found that when learning transitioned online, students’ main issue was engagement whereas prior to the pandemic the main issue for students was content ( Perets et al., 2020 ). The lack of peer connection and technological issues seem to be significant problems for students during online learning and could contribute to students’ issues with engagement. The problems with attention during an online lecture might be attributed to the lack of social accountability that an in-person lecture promotes to put away distractions like cell phones and taking active notes. Additionally, CAPE data shows that students rate their professors’ efforts and course design highly and similarly before and during Fall 2019 and Fall 2020. Although every course and professor has different requirements, creating collaborative opportunities and incorporating interactive features into lectures could be beneficial to student engagement.

For live lectures, the increase in students reporting skipping live online lectures more often may be due to the increase in availability and ease of recorded options with online lectures. A similar study to this research found that when the university transitioned to Pass/No Pass grading rather than letter grading during ERT, students attended synchronous lectures less ( Perets et al., 2020 ). During the pandemic, the institution’s deadline to change to P/NP grading was extended and more academic departments allowed Pass/No Pass classes to fulfill course requirements. In our study, we did not detect an increase in students who took advantage of the P/NP grading, but it is possible that students skipped more synchronous lectures knowing that they could use the Pass option as a safety net if they did not dedicate the typical amount of lecture time to learn the material. The results emphasize the vital role of the cognitive dimension in engagement.

It is clear that more students are taking advantage of recorded options with online learning. A survey of Harvard medical students indicates a preference for the recorded option because of the ability to increase the speed of the lecture video and prevent fatigue ( Cardall et al., 2008 ). Consistent with previous research, our results suggest that students may seek more value and time management options from course material when classes are fully online ( Perets et al., 2020 ). Recorded lectures allow freedom for students to learn at a time that works best for them ( Rae and McCarthy, 2017 ). For discussions, students reported that they had more discussions that were live rather than recorded. Research indicates that successful online learning requires strong instructor support ( Dixson, 2010 ; Martin and Bolliger, 2018 ). The smaller class setting of a discussion, even virtual, may promote better engagement through interaction among the students, content, and the discussion leader.

Based on CAPE results, which are conducted the week before final exams, students expected higher grades during the online learning period. Although expected grades rose, students concur with previous surveys that the workload was overwhelming and was not adequately adjusted to reflect the circumstances of ERT ( Hussein et al., 2020 ; Shin and Hickey, 2020 ). While there are many factors that could account for this, including the fact that expected grades reported on CAPE do not reflect a student’s actual grade, one possible explanatory factor is the use of more lenient grading standards and course practices during the pandemic. In addition to relaxed Pass/No Pass standards, courses were more likely to adopt practices like open-book tests or no-fault finals, providing students with assessments that emphasized a demonstration of deeper conceptual understanding rather than memorization. It is important to note that students’ perceptions of their learning does not indicate that students are actually learning or performing better academically. This goes for the CAPE question, “I learned a great deal from this course,” the CAPE question about expected grades, and the small scale survey reports about academic performance. We took interest in these questions because they offer insight into the level of difficulty students perceived during ERT due to the shift in engagement demands from remote learning. More research should be done with students’ academic performance data before and after ERT to clarify whether there was a change in students’ learning.

Students’ preference for using a virtual platform during lecture to ask, answer, and respond to questions was surprising. This extends previous evidence from Vu and Fadde (2013) , who found that, in a graduate design course at a Midwestern public university with both in-person and online students in the same lecture, students learning online were more likely to ask questions through a chat than students attending in-person lectures. In addition, during the COVID-19 pandemic, Castelli and Sarvary (2021) report that Zoom chat facilitates discussions for students, especially for those who may not have spoken in in-person classes.

When students were surveyed on the issues they faced with online learning, the most common issues had to do with engagement in lectures, interaction with instructors and peers, and having a poor physical work environment, while technical issues or issues with learning platforms were less common. The distinction between frequency and impact is key, since issues such as bad WiFi connection can be debilitating to online learning even if uncommon, and issues with technology and physical environment also correlate with equity concerns. Other surveys have found that students and faculty from equity-seeking groups faced more hardships during online learning because of increased home responsibilities and problems with internet access ( Chan et al., 2020 ; Shin and Hickey, 2020 ). Promoting student engagement in class involves more than well-planned teaching strategies. Instructors and universities need to look at the resources and accessibility of their class to reduce the digital divide.

According to the CAPE data from Table 2 , instructors received consistent reviews before and after the ERT switch, indicating that they maintained their effectiveness in teaching. The ratings for two CAPE prompts “Instructor is well prepared for class” and “Instructor starts and finishes class on time” had statistically significant decreases from Fall 2019 to Fall 2020. This decrease could be attributed to increased technological preparation needed for online courses and the variety of offerings for lecture modalities. For example, some instructors chose to offer a synchronous lecture at a different time than the original scheduled course time, and then provide office hours during their scheduled lecture time to discuss and review the lectures. Regardless of the statistically significant changes, the means for these two statements are high and similar to Fall 2019.

What Engagement Tools Are Being Utilized in Lectures and What do Students Think About Them?

Based on the results, a majority of students report that their professors are using weekly quizzes, breakout rooms, and polls at least sometimes in their classes to engage students. Students had highly positive ratings of in-course polling, were mostly neutral or positive about weekly quizzes (as a replacement for midterm or final exams), but were slightly negative about breakout rooms. Venton and Pompano (2021) report positive qualitative student feedback from students in chemistry classes at the University of Virginia, with some students finding it easier to speak up and make connections with peers than in an entire class; Fitzgibbons et al. (2021) , meanwhile, found in a sample of 15 students at the University of Rochester that students preferred working as a full class instead of in breakout rooms, though students did report making more peer connections in breakout rooms. Breakouts have potential to strengthen student-student and student-instructor relationships, but further research is needed to clarify their effectiveness.

Changes were also made to course structure, with almost all (94%) of students reporting that open-book exams were used at least sometimes. Open-book exams were also the most popular intervention overall, although the reason for their widespread adoption (academic integrity and fairness concerns) is likely different from the reasons that students like them (less focus on memorization). Open-book tests, however, present complications. Bailey et al. (2020) notes that while students still needed a good level of understanding to succeed on open-book exams, these exams were best suited to higher-order subjects without a unique, searchable answer.

Changes were detected in the responses to the CAPE statements, “Assignments promote learning,” “The course material is intellectually stimulating,” and “I learned a great deal from this course,” noted in Table 3 . Although there were statistically significant changes detected by the Mann–Whitney U Test, the means between Fall 2019 and Fall 2020 are still similar and positive. The results from this table indicate that students felt that there was not a decrease in learning and interest in their material. This might be due to instructors changing the design of assessments and assignments to accommodate for academic integrity and modality circumstances in the online learning format. The consistently positive CAPE ratings are also likely due to the fact that students are aware that CAPEs are an important factor for the departments’ hiring and retention decisions for faculty, and subsequently important for their instructors’ careers. Students may have also recognized that most of the difficulties in the switch to online learning were not the instructors’ fault. Students’ sympathy for the challenges that instructors faced may be contributing to the slightly more positive reviews during Fall 2020.

One of the most common experiences reported by students was a decrease in interaction with peers, with a strong majority of students saying that a lack of peer interaction hurt their learning experience. A study from Central Michigan University shows that peer interaction through in class activities supports optimal active learning ( Linton et al., 2014 ). Without face-to-face learning and asynchronous classes during COVID, instructors were not able to conduct the same collaborative activities. When asked how students interacted with their peers, the most common responses were student-run course forums or texting. This seems to support the findings of Wong (2020) which indicated that during ERT, students largely halted their use of synchronous forms of communication and opted instead for asynchronous ones, like instant messaging, with possible impacts on students’ social development. Students also reported a decrease in interaction with their instructors with a plurality saying that a lack of access to their instructors affected their academic experience. At the same time, ratings of professors’ ability to accommodate for the issues students faced during online education were high, as were students’ ratings of online office hours. It seems that students sympathized with instructors’ difficulties in the ERT transition but were aware that the lack of instructor presence impacted their learning experience nonetheless.

Limitations

There are some limitations in this study that should be considered before generalizing the results more widely. The survey was conducted at just a single university, UCSD: a large, highly-ranked, public research institution in the United States with its own unique approach to the COVID-19 pandemic. These results would likely differ significantly for online education at other universities. In addition, though care was taken to distribute the survey in channels used by all students, the voluntary response of students chosen from these channels does not constitute a simple random sample of undergraduates attending this institution. For example, our survey over-represents female students, who constituted 72.7% of the survey sample. The channels chosen could also bias certain results; for example, it is possible that students who answer online surveys released on the institution’s social media channels are less likely to have technical or Internet difficulties. Results from the small survey might be skewed slightly because respondents had to recall a year prior to their experiences in Fall 2019, whereas they might have had a more accurate memory of their Fall 2020 experience. CAPEs are completed at the end of the quarter when their recollection of their experiences is fresh, so those reviews are likely less susceptible to this unconscious bias.

The issues with sampling are somewhat mitigated in the CAPE data, but these responses are not themselves without issue. CAPE reviews are still a voluntary survey, and therefore are not a random sample of undergraduates. In addition, some instructors use extra credit to incentivize students to participate in CAPEs if the class meets a threshold percentage of responses, which might skew the population of respondents. CAPE responses tend to be relatively generous and positive, with students rating instructors and educational quality much higher in CAPE reviews than in our survey. This is possibly because the CAPE forms make it easy for students to report the most positive ratings on every item without considering them individually. Additionally, students are aware that CAPEs have an impact on the department’s decisions to rehire instructors.

Teaching Implications

Online learning presented multiple challenges for instructors and students, illuminating areas to improve in higher education that were not recognized before the COVID-19 pandemic. A majority of students expressed their comfort in engaging with the Zoom chat and polling. Students might feel this way since they can ask and answer questions using the chat feature without disrupting the focus in class. Therefore, in both further online learning and in-person classes, instructors might be able to stimulate interaction by lowering the social barriers to asking and answering questions. Applications such as Backchannel Chat, Yo Teach!, and NowComment offer more features than Zoom or Google Meet to prevent fatigue and increase retention in-person or online ( LearnWeaver, 2014 ; Hong Kong Polytechnic University, 2018 ; Paul Allison, 2018 ).

At the same time, increased interactivity in lectures, especially if required, is not necessarily a panacea for engagement issues. For example, some professors might require students to turn on their cameras, increasing accountability and giving an incentive to visibly focus as if in an in-person classroom. However, Castelli and Sarvary (2021) found, as we did, that the majority of students in an introductory collegiate biology course kept their cameras off; students cited concerns about their appearance, other people being seen behind them, and weak internet connections as the most common reasons for not keeping cameras on. Not only are these understandable concerns, but they correlate with identity as well: Castelli and Sarvary found that both underrepresented minorities and women were more likely to indicate that they worried about cameras showing others their surroundings and the people behind them.

Prior to the COVID-19 pandemic, online learning was a choice. Our research demonstrates that online learning has a long way to go before it can be used in an equitable manner that creates an engaging environment for all students, but that instructors adapted well to ERT to ensure courses promoted the same level of learning. The sudden nature of remote learning during the COVID pandemic did not allow for instructors or institutions to research and promote the most engaging online learning resources. Students have widely varying opinions and experiences with their higher education online learning experience during the pandemic. Our data analysis shows that distance learning during the pandemic had a toll on attendance during live lecture and peer-instructor connection. The difference in expected grades from Fall 2019 to Fall 2020 indicates that students felt differently about their ability to succeed in their online classes. In addition, students had trouble managing work loads during online learning. We gathered that instructors could be using engagement strategies more often to match students’ enthusiasm for those strategies, such as chat features and polls. Despite the challenges of online learning highlighted, this research also presents evidence that online learning can be engaging for students with the right tools. Student reviews indicated similarity before and after the switch to online learning, including indicating that course assignments promoted learning and the material was intellectually stimulating. These results propose that the courses and professors, despite the modality switch and changes to teaching and assessment strategies, maintained the level of learning that students felt they were getting out of their course.

Data Availability Statement

The data supporting the conclusions of this article contains potentially identifiable information. The authors can remove this identifying information prior to sharing the data.

Author Contributions

BH contributed to this project through formal analysis, investigation, and writing. PN contributed to the project through formal analysis, investigation, visualization, and writing. LC contributed to conceptualization, resources, supervision, writing, review, and editing. SH-L contributed methodology, supervision, writing, review, and editing. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We would like to acknowledge the Qualcomm Institute Learning Academy for supporting this project.

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Keywords : student engagement, undergraduate, online learning, in-person learning, remote instruction and teaching

Citation: Hollister B, Nair P, Hill-Lindsay S and Chukoskie L (2022) Engagement in Online Learning: Student Attitudes and Behavior During COVID-19. Front. Educ. 7:851019. doi: 10.3389/feduc.2022.851019

Received: 08 January 2022; Accepted: 11 April 2022; Published: 09 May 2022.

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*Correspondence: Brooke Hollister, [email protected]

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COVID-19, students satisfaction about e-learning and academic achievement: Mediating analysis of online influencing factors

Muhammad younas, xiaoyong zhou, rashid menhas.

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Edited by: Haitao Wu, Beijing Institute of Technology, China

Reviewed by: Cunyi Yang, Sun Yat-sen University, China; Xiaodong Yang, Xinjiang University, China

*Correspondence: Xu Qingyu [email protected]

This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

Received 2022 May 19; Accepted 2022 Jul 29; Collection date 2022.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

The current study examines student satisfaction with e-learning, the adaption of online learning channels, digital competency of students' involvement, and academic achievement during COVID-19.

The purpose of this study is to examine the online influencing components for learning among University students in Pakistan during the COVID-19 Pandemic.

The study population comprised Pakistani University students in Punjab province who tooke online lessons throughout the epidemic. In accordance with the study's purpose, a questionnaire survey was employed to gather primary data. SPSS-23 is used for analyzing the demographic data, and cleaning and preparing data for testing hypotheses. SmartPLS 3.0 was used to investigate the suggested study framework using structural equation modeling (SEM).

The analysis of the SEM model shows that all planned hypotheses (Adaptation of Online Education Channels -> Satisfaction about E-learning, COVID-19 Pandemic -> Adaptation of Online Education Channels, COVID-19 Pandemic -> Digital Competence, COVID-19 Pandemic -> Motivation for Online Learning, COVID-19 Pandemic -> Willingness for Online Learning, Digital Competence -> Satisfaction about E-learning, Motivation for Online Learning -> Satisfaction about E-learning, Satisfaction about E-learning -> Academic Achievement, Willingness for Online Learning -> Satisfaction about E-learning) are confirmed.

The results linked e-learning satisfaction to academic success and Pakistani students who utilized e-learning throughout the outbreak reported higher levels of academic satisfaction and achievement.

Keywords: e-learning, students satisfaction, academic achievement, online influencing factors, COVID-19

Introduction

The World Health Organization (WHO) considered COVID-19 a pandemic in 2020, impacting millions of children and educators globally, and various technological applications have been deployed in the epidemic age to facilitate learning (Nainggolan, 2021 ; Shahzad et al., 2021 ). Online learning motivation is a mediating factor in the COVID-19 pandemic condition; when conventional learning/teaching are no longer viable options, online learning may help students continue their education (Adedoyin and Soykan, 2020 ; Rahman et al., 2021 ). In a less digitalized economy, mostly higher education institutions changed from face-to-face learning to emergency remote teaching, emphasizing the enabling variables that impact the deployment of e-learning systems during the COVID-19 epidemic (Jan, 2020 ; Edem Adzovie and Jibril, 2022 ). The educational integration of technology was tested because of the necessity to establish emergency remote education due to COVID-19 which was implemented to keep students and instructors educated during challenging periods of lockdown (Wang et al., 2020 ; Valverde-Berrocoso et al., 2021 ).

Students' satisfaction with e-learning during COVID-19

Before the COVID-19 epidemic, researchers looked at what made students stay with online learning. As a result of pandemic circumstances, students' participation in academic activities improves before favorably impacting their contentment (Kim and Kim, 2021 ; Dalipi et al., 2022 ; Ngah et al., 2022 ). During the COVID-19 crisis, informal digital learning is critical for students as it investigates the link between digital competency and academic participation in higher education from diverse cultural backgrounds (He and Li, 2019 ; Heidari et al., 2021 ). In this unexpected time, the swift migration from conventional face-to-face learning to online learning has been seen as a standard revolution in higher education (Shah et al., 2021 ). With the COVID-19 epidemic, digital technologies have become an inescapable and vital aspect of learning, and colleges worldwide have suddenly halted face-to-face teaching and resorted to technology-mediated teaching (Okoye et al., 2021 ; Vladova et al., 2021 ). Some studies examine the student e-learning satisfaction during the COVID-19 epidemic and the mediation effects of crucial variables, including learning stress and motivation to learn (Mohd Satar et al., 2021 ; Ong et al., 2021 ).

Students' motivation and willingness for e-learning

Universities have started educating students online and are keen to achieve improved learning results utilizing e-interactive learning capabilities (Abou El-Seoud et al., 2014 ). As part of the COVID-19 study, researchers look at the attitudes of students as well as academic staff toward online learning and distance education and the aspects that influence students' preparedness to continue using an online learning system (Falfushynska et al., 2021 ; Mabrur et al., 2021 ; Ngah et al., 2022 ). Preliminary studies show that e-influence, learning students' interest in utilizing e-learning resources, and their performance and motives impacted their desire to embrace e-learning (Chang et al., 2017 ; Radha et al., 2020 ). China has initially employed e-learning strategies by marketing and executing online learning courses in Chinese colleges and universities, and students have willingly accepted this move as new normal (Jin et al., 2021 ). Previous research analyzes the primary elements that encourage medical students' acceptance and opinions of e-learning during the COVID-19 closing period (Almaiah et al., 2020 ; Ibrahim et al., 2021 ). However, despite the problems that come with such a dramatic shift in education, e-learning has shown to be the best solution to the COVID-19 epidemic (Hamdan et al., 2020 ; Maatuk et al., 2022 ). The present research investigates the association between student satisfaction with e-learning and academic achievement during COVID-19 in developing nations. Second, the study addresses the adaptation of online learning channels and digital competence of students' appointments in the association between students' satisfaction with e-learning and academic achievements.

Literature review

In response to the COVID-19 epidemic, China urged millions of full-time students to resume online. Indonesian education examines the necessity for an online model during the COVID-19 epidemic and how educational institutions might employ current resources to bring formal education online (Li and Lalani, 2020 ; Manca and Meluzzi, 2020 ; Cahaya et al., 2022 ). Educational institutions have been compelled to adopt alternate teaching and learning methodologies during the present epidemic, including a complete transfer to online learning. Both professors and students feel that online education is beneficial (Almahasees et al., 2021 ; Anderton et al., 2021 ). The continuing COVID-19 epidemic forced the cessation of educational operations, revealing both enabling and hindering elements for successful learning and communication technology integration into online course design and delivery (Khalil et al., 2020 ; Meletiou-Mavrotheris et al., 2021 ). Various barriers prevent underprivileged students from entirely using the potential of e-learning, and studies show a statistically significant association between internet gadget availability and utilization (Mpungose, 2020 ; Segbenya et al., 2022 ).

During the pandemic of COVID-19, it examined and assessed the impact that e-learning had on the psychological distress of students (Hasan and Bao, 2020 ). COVID-19 demanded restraint and isolation, affecting teacher–student interactions. Computer-based learning has replaced classroom education and one-on-one engagement. During the continuing COVID-19 epidemic, it is important to analyze University students' perceptions and preparation for online learning (Khan et al., 2021 ). It is the goal of the investigation to look at how students' online learning experiences and overall satisfaction with their University's brand image are influenced by the use of ICT (Shehzadi et al., 2021 ; Younas et al., 2022 ). Several studies examined how college students see themselves as adopting, using, and accepting emergency online learning and the implementation of their online system, and how they make proper decisions to allow more University students to embrace e-learning (Jameel et al., 2020 ; Patricia Aguilera-Hermida, 2020 ). Technological advances have affected web technologies and the e-learning process and demand initiatives to leverage the full capabilities of technical innovation to improve e-learning systems and their benefits (Yaw Obeng and Coleman, 2020 ).

Online teaching helps higher education to reduce cross-contagion in classrooms, and it measures online student satisfaction to assure online teaching quality during the pandemic (Zhang et al., 2021 ). Online learning is becoming increasingly common during the COVID-19 epidemic period as a means of enhancing students' academic performance in the target language, while student engagement has been found to be an essential factor, and these studies review the literature on online teaching and learning strategies in teacher education (Carrillo and Flores, 2020 ; Luan et al., 2020 ). A number of studies have looked at the connections between students' ability to self-regulate, their overall well-being, and their academic performance when taking campus-based courses via distance learning and how teachers can use this cutting-edge technology to get better results from their students by incorporating text, sounds, and visual graphics into their lessons (Muhammad et al., 2020 ; Yavuzalp and Bahcivan, 2021 ). To affect students' motivation and aspirations, the usage of e-learning has risen significantly in just a few years and emphasizes the significance of ongoing education for instructors (Paechter et al., 2010 ).

Statement of the study

It is worth mentioning that students' satisfaction with e-learning is directly linked to their academic performance or achievement, and it may also be used to assess the effectiveness of online courses (Alqurashi, 2019 ). In a developing country like India, where device suitability and bandwidth availability are problems, e-learning planning, design, and effectiveness remain unknown (Muthuprasad et al., 2021 ). Understanding student satisfaction with e-learning and its relationship to motivation and willingness through adopting online channels and digital competence will primarily assist students in achieving better academic achievements. Perceived obstacles and COVID-19 knowledge directly affect students' intentions, but these impacts are further mediated by perceived utility and ease of use of e-learning technologies (Nikou and Maslov, 2021 ). The research is significant because it provides educators and policymakers with a fresh viewpoint on how to properly plan for the introduction of e-learning while keeping student satisfaction in mind. Hypothesized correlations between research variables are shown in Figure 1 .

Study conceptual framework.

Study hypotheses

H1: Motivation for online learning positively impacted the satisfaction about e-learning during the COVID-19 pandemic.

H2: Willingness for online learning positively impacted the satisfaction about e-learning during the COVID-19 pandemic.

H3: Adoption of online education channels positively impacted the satisfaction about e-learning during the COVID-19 pandemic.

H4: Digital competence positively impacted the satisfaction about e-learning during the COVID-19 pandemic.

H5: Academic achievement has a positive association with motivation for online learning.

H6: Academic achievement has a positive association with willingness for online learning.

H7: Academic achievement has a positive association with the adoption of online education channels.

H8: Academic achievement has a positive association with the digital competence.

Research methods

Study locale.

The present investigation took place in Pakistan's Punjab province and was exploratory. The research was authorized by the Ethical Committee of Soochow University in Suzhou, Jiangsu before the final data was collected. The participants' involvement in the research was voluntary, and they gave their informed permission before the study began.

Study design

This study used a questionnaire-based research approach to examine the association between Pakistani University students' e-learning satisfaction and academic achievement. The study took place in six cities in Punjab Province of Pakistan, 550 male and 650 female students with different age groups and educational backgrounds participated in this online survey and a well-designed questionnaire was used to obtain the main data. Five-point close-ended Likert scale questions were used in the survey.

Participants

Between December 2021 and January 2022, the online survey of Pakistani University students was undertaken. Before the final survey, 50 respondents were pre-tested for response rate. After pre-testing, certain questions were changed to improve response rates. In total, 1,200 participant answers were evaluated after data quality assessment. Respondents have to meet the following criteria: (1) Pakistani University students who attended online courses during COVID-19 and (2) those who volunteered for this research. The convenient sampling technique was used to gather the primary data, and University students from six different cities of Punjab Province in Pakistan, namely, Lahore, Multan, Rawalpindi, Faisalabad, Bahawalpur, and Sialkot have fulfilled the inclusion criteria for this study.

Operationalization of study variables

There are seven variables used in this study to investigate e-learning satisfaction and academic achievements of University students. The study confined one independent variable (COVID-19 Pandemic), four mediators (motivation for online learning, willingness for online learning, adaptation to online learning channels, and digital competence), and two dependent variables (satisfaction with e-learning and academic achievement).

Demographic information

The demographic questions were about gender (male and female), age (20–25, 25–30, 30–35, 35+ years old), education (undergraduate, graduate, doctoral, or vocational degree), and year of attending the University (1–2, 3–4, +4 years).

COVID-19 pandemic and online learning impact

Due to COVID-19, the students' online learning impact was calculated by adopting five items developed questions. Studying e-learning from students' and instructors' perspectives on using and developing e-learning systems at public universities while dealing with the COVID-19 epidemic has become more critical than ever before (Pokhrel and Chhetri, 2021 ; Maatuk et al., 2022 ). For this study, the courses were replaced with “online learning,” which impacted students' learning achievements. The participants used a five-point Likert scale ranging from 1 to 5.

Students' satisfaction with e-learning

E-learning is a technology-driven distant learning tool. The studies sought to investigate undergraduate students' e-learning experience and identify variables impacting student and teacher satisfaction (Elshami et al., 2021 ; Giray, 2021 ). During the COVID-19 epidemic, when people were compelled to remain home, e-learning was the only way to learn. It is important to understand e-learners' perceptions and satisfaction with e-learning technologies.

Data analysis and results

Structural equation modeling (SEM) of SmartPLS 3.0 was used to examine the measurement and structural models as it analyses intricate models that include both observable and latent components (Sharif et al., 2021 ). PLS-SEM may support SEM results with almost any degree of structural complexity, including higher-order structures that generally alleviate multicollinearity concerns (Ringle et al., 2015 ). The SmartPLS was used to evaluate the measurement and structural models and primary data can be analyzed using this tool (Hair et al., 2021 ). The SmartPLS research design is a reliable, scalable, and advanced method for developing a significant statistical model, and SmartPLS functions SEM's aid in achieving the intended goal (Abbas et al., 2019 ). SEM determines the model's discriminant, convergent, and average variance for each construct using factor loadings (Pahlevan Sharif and Mahdavian, 2015 ). Various connections between variables in the conceptual model may be studied using multivariate analytic methods.

Descriptive statistics

The descriptive statistics in Table 1 displays that 54.17% of the study participants were female University students and 45.83% were male University students. The age of participants shows that the majority of the students were 20–25 (60.41%), 25–30 (18.33%), 30–35 (12.5%), and 35+ (8.75%) years old. Moreover, 70.83% of the participants were undergraduate students, 16.66% were graduates, 3.75% were PhD students, and 8.75% were with vocational degrees. Around 54.5% of students stated having four to five online classes per week and 21.33% of students reported three to four classes per week, and the rest of them reported one or two online classes weekly during a pandemic. Regarding their studying years in the University, 39.83% of them were in their first year and 41.66% were in their second year, and the rest of them were in their third and fourth years of studies.

Demographic information of study participants (N-1200).

Multivariate analysis

Measurement model assessment.

The statistical results of this study model are shown in Table 2 . The reliability of the survey was measured using alpha values. According to Nunnally ( 1994 ) and She et al. ( 2021 ), the established value of alpha for measuring dependability is over 0.70, and each component is judged trustworthy according to the standard and Cronbach's alpha values ranging from 0.700 to 0.880. Composite reliability (CR) values for each construct were calculated ranging from 0.796 to 0.922. In this study, loading values routinely exceeded 0.60. The standard range of average variance extracted (AVE) is described as 0.50 (Anonymous, 2018 ). For discriminant validity, the square root of each construct's AVE should be greater than its connection with other constructs (Fornell and Larcker, 1981 ). This study's AVE values were more significant than the average range (from 0.500 to 0.797). To test multicollinearity, the variation influence factor (VIF) must be <1.000; 0.500 is acceptable. All formative constructions exhibited VIFs <0.800, indicating no multicollinearity.

Results of the measurement model of research (N-1200).

Discriminant validity

It was shown that discriminant validity (DV) could be used to quantify concepts that had no connection to one another conceptually. Discriminant validation aims to show any evidence of discrimination based on the differences between all components (Campbell and Fiske, 1959 ). DV was used to evaluate and describe unrelated constructs. DV also gives the verification of all measures concerning component dissimilarity. When defining measure correspondence, DV entails evaluating non-statistically related components. DV may be calculated using a factor's AVE. The DV showed that the square root of each construct and AVE was greater than its connection with other constructs ( Table 3 ; Figure 2 ).

Discriminant validity assessment results (N-1200).

Structural equation model

It asserts that the structural model serves as the theoretical framework for using structural equations to assess the inner path model (Skrondal and Rabe-Hesketh, 2004 ; Chin, 2010 ). All assumptions were tested using (SEM) of SmartPLS 3.0. Model fitness was measured using standardized root-mean-square-residual (SRMR) which is a standardized-residuals index that evaluates model fitness, chi-square, and normed fit index (NFI) (Brown, 2006 ; Chen, 2007 ). The SRMR value compares observed covariance with predicted matrices. SRMR values of 0.08 or less may be used. The predicted SRMR value is 0.0510, which is adequate as a model fit. The NFI is 0.505, and the chi-square (2) value is 14086.307, as seen in Table 4 .

Model fit summary (N-1200).

SRMR, Standardized-root-mean-square-residual; d_ULS, unweighted least squares discrepancy; d_G, geodesic discrepancy; X 2 = chi-square; NFI, normed fit index.

The standard beta was utilized to determine the significance of the hypotheses, and the beta value indicates how distinct variables may differ. The hypothesized research model was used to obtain the standardized beta (β) value for each connection ( Table 5 ). The importance of endogenous latent variables will be judged crucial if beta (β) values are large and significant. The importance of each path's beta value was determined using T-statistics. The bootstrapping method was used to determine the significance of the beta (β) value and examine the relevance of assumed connections. Table 5 illustrates the recommended structural model connections and (β) statistics. SmartPLS-bootstrapping research variable t -values show the SmartPLS-bootstrapping t -values for the research variables ( Figure 3 ).

Final results of standard beta, t-statistics, and p -values (N-1200).

PLS-Bootstrapping, T -Values.

The result (HI: β = 0.213, t = 11.637, p = < 0.0000) reveals that the adaptation of online education channels have positive impact on satisfaction about e-learning. H2 results (β = 0.681, t = 35.083, p = < 0.0000) proved that COVID-19 pandemic has significant positive impact on the adaptation of online education channels. H3 results (β = 0.634, t = 31.322, p = < 0.0000) statistically reveals that COVID-19 pandemic has positive association with digital competence. H4 statistically documented that (β = 0.672, t = 41.796, p = < 0.0000) COVID-19 pandemic have positive effects on motivation for online learning. H5 supported that (β = 0.652, t = 35.325, p = < 0.0000) COVID-19 pandemic positively influences on students' willingness for online learning. H6 reveals that (β = 0.130, t = 3.768, p = < 0.0000) digital competence has positive association with satisfaction about e-learning. The result of H7 statistically documented that (β = 0.295, t = 11.228, p = < 0.0000) motivation for online learning has positive influence on satisfaction about e-learning. H8 results shows that (β = 0.725, t = 72.787, p = < 0.0000) satisfaction about e-learning has significant association with academic achievement and H9 reveals that (β = 0.398, t = 11.606, p = < 0.0000) willingness for online learning has positive association with satisfaction about e-learning.

This research investigates students' e-learning satisfaction and academic achievement to evaluate online influencing elements for learning among University students in Pakistan during the COVID-19 pandemic. The analysis of the SEM model shows that all planned hypotheses (H1: Adaptation of Online Education Channels -> Satisfaction about E-learning, β = 0.213, t = 11.637, p = < 0.0000; H2: COVID-19 Pandemic -> Adaptation of Online Education Channels, β = 0.681, t = 35.083, p = < 0.0000; H3: COVID-19 Pandemic -> Digital Competence, β = 0.634, t = 31.322, p = < 0.0000; H4: COVID-19 Pandemic -> Motivation for Online Learning, β = 0.672, t = 41.796, p = < 0.0000; H5: COVID-19 Pandemic -> Willingness for Online Learning, β = 0.652, t = 35.325, p = < 0.0000) are confirmed. Many students may suffer from psychological repercussions due to the sudden move from face-to-face classrooms and interactions with peers and professors to online learning (Lufungulo et al., 2021 ; Lim et al., 2022 ). During the COVID-19 pandemic, which had a significant impact on public health and education systems around the world, this study examines how public University students perceived online classes, measuring their research self-efficacy and course satisfaction before and after their procedures and results are consistent with previous studies (Randazzo et al., 2021 ; Sarkar et al., 2021 ). Since the COVID-19 pandemic, online teaching has been encouraged, all institutions have accepted it, and the COVID-19 pandemic has had an unparalleled influence on education globally. This gap has several dimensions, including access to gadgets and the internet and other external variables, such as parental support, teacher quality, and the learning environment (Coleman, 2021 ; Wu, 2021 ).

The findings revealed a link between student satisfaction with e-learning and academic accomplishment. According to the data, Pakistani students who used e-learning throughout the epidemic had greater learning satisfaction and academic achievement. The features of students' involvement in online learning remain understudied, and the factors impacting students' satisfaction and achievement in online courses during the COVID-19 pandemic era and the link between these variables have to be identified and established (Gopal et al., 2021 ; Salas-Pilco et al., 2022 ). All hypotheses (H6: Digital Competence -> Satisfaction about E-learning, β = 0.130, t = 3.768, p = < 0.0000; H7: Motivation for Online Learning -> Satisfaction about E-learning, β = 0.295, t = 11.228, p = < 0.0000; H8: Satisfaction about E-learning -> Academic Achievement, β = 0.725, t = 72.787, p = < 0.0000; H9: Willingness for Online Learning -> Satisfaction about E-learning, β = 0.398, t = 11.606, p = < 0.0000) statistically reveals the positive association in all. For future academics, several studies provide the directions and consequences for online education by identifying elements that impact online learning satisfaction at home as a result of the COVID-19 pandemic and getting information on difficulties in the teaching/learning process (Trisanti et al., 2021 ; Kornpitack and Sawmong, 2022 ). These studies provide a new understanding of learner interaction and its relationship to course content, teaching methods, and learning satisfaction in an Asian context. Students' satisfaction with online learning may be influenced by self-motivation and the usage of online tests as a form of evaluation (Basuony et al., 2020 ; Thach et al., 2021 ). By providing actual information on the components that encourage students' preparedness to continue online learning during the COVID-19 pandemic lockdown, new insights into the literature on students' desire to continue online learning were achieved (Mohd Satar et al., 2020 ; Ngah et al., 2022 ).

With the increased use of e-learning during the pandemic period and the introduction of various technology applications and their impact on student satisfaction and academic achievement as online influencing factors, the entire domain of learning and education in Pakistan and around the world has changed as research indicated that e-learning has a significant relationship between student satisfaction and academic achievement. Pakistani students who utilized e-learning during the outbreak had higher learning satisfaction and academic achievement. The findings showed a correlation between e-learning satisfaction and academic achievement, with Pakistani students who used e-learning during the epidemic reporting better levels of academic achievement. The study is noteworthy because it offers educators and policymakers a new perspective on adequately preparing for the adoption of e-learning while ensuring student satisfaction.

Study limitations and future scope

The study is not without limitations. The research sample consisted of University students from five renowned cities in the Punjab province of Pakistan, which did not reflect the whole population of Pakistani University students. Future studies may use longitudinal or experimental designs to give further evidence regarding observed correlations and their underlying processes. Future research should evaluate our concept in diverse circumstances, such as student e-learning satisfaction. A prospective study evaluating this approach may also include online influencing factors affecting students' satisfaction with e-learning.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by School of Education, Soochow University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

XQ is the principal investigator. MY collected data, wrote the article, and analyzed the data. XZ guided the psychological perspective and methodology. UN and RM designed the study model and hypothesis, contributed in discussion section, and proofreading and finalizing the manuscript. All authors contributed to the article and approved the submitted version.

This study was supported by the Project Research on the Construction Quality of Research Groups in Universities in China approved by the National Office for Education Sciences Planning China (Project No: BIA200166).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We thank all the researchers for data collection and processing and acknowledge our study's survey participants.

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Open Access

Peer-reviewed

Research Article

An observational study of engineering online education during the COVID-19 pandemic

Roles Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Biomedical Engineering, California State University, Long Beach, California, United States of America, Department of Computer Engineering and Computer Science, California State University, Long Beach, California, United States of America

Roles Conceptualization, Investigation, Writing – original draft, Writing – review & editing

Affiliation Department of Computer Engineering and Computer Science, California State University, Long Beach, California, United States of America

Roles Conceptualization, Investigation, Writing – review & editing

Affiliation Department of Civil Engineering and Construction Engineering Management, California State University, Long Beach, California, United States of America

Affiliation Department of Chemical Engineering, California State University, Long Beach, California, United States of America

Roles Conceptualization, Supervision, Writing – review & editing

Affiliations Department of Civil Engineering and Construction Engineering Management, California State University, Long Beach, California, United States of America, College of Engineering, California State University, Long Beach, California, United States of America

• Shadnaz Asgari, 
• Jelena Trajkovic, 
• Mehran Rahmani, 
• Wenlu Zhang, 
• Roger C. Lo, 
• Antonella Sciortino

• Published: April 15, 2021
• https://doi.org/10.1371/journal.pone.0250041
• Reader Comments

The COVID-19 pandemic compelled the global and abrupt conversion of conventional face-to-face instruction to the online format in many educational institutions. Urgent and careful planning is needed to mitigate negative effects of pandemic on engineering education that has been traditionally content-centered, hands-on and design-oriented. To enhance engineering online education during the pandemic, we conducted an observational study at California State University, Long Beach (one of the largest and most diverse four-year university in the U.S.). A total of 110 faculty members and 627 students from six engineering departments participated in surveys and answered quantitative and qualitative questions to highlight the challenges they experienced during the online instruction in Spring 2020. Our results identified various issues that negatively influenced the online engineering education including logistical/technical problems, learning/teaching challenges, privacy and security concerns and lack of sufficient hands-on training. For example, more than half of the students indicated lack of engagement in class, difficulty in maintaining their focus and Zoom fatigue after attending multiple online sessions. A correlation analysis showed that while semi-online asynchronous exams were associated with an increase in the perceived cheating by the instructors, a fully online or open-book/open-note exams had an association with a decrease in instructor’s perception of cheating. To address various identified challenges, we recommended strategies for educational stakeholders (students, faculty and administration) to fill the tools and technology gap and improve online engineering education. These recommendations are practical approaches for many similar institutions around the world and would help improve the learning outcomes of online educations in various engineering subfields. As the pandemic continues, sharing the results of this study with other educators can help with more effective planning and choice of best practices to enhance the efficacy of online engineering education during COVID-19 and post-pandemic.

Citation: Asgari S, Trajkovic J, Rahmani M, Zhang W, Lo RC, Sciortino A (2021) An observational study of engineering online education during the COVID-19 pandemic. PLoS ONE 16(4): e0250041. https://doi.org/10.1371/journal.pone.0250041

Editor: Mohammed Saqr, KTH Royal Institute of Technology, SWEDEN

Received: November 22, 2020; Accepted: March 30, 2021; Published: April 15, 2021

Copyright: © 2021 Asgari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This research is partially supported by CSULB Champions program through Coronavirus Aid, Relief, and Economic Security (CARES) Act funding.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Engineering education has been traditionally content-centered, hands-on, design-oriented, and focused on the development of critical thinking or problem-solving skills [ 1 ]. Various pedagogical methodologies have shown efficacy in enhancement of engineering education including active learning [ 2 ], flipped classroom [ 3 ] and project-based learning [ 4 – 6 ]. Over the last decade, online education has become a viable component of higher education in engineering subfields such as electrical and computer engineering, computer science and information technology especially at the master’s or post-graduate level [ 7 ].

Although the online education has not been a new concept to educators in general, the COVID-19 pandemic introduced an unprecedented and global need to explore online teaching/learning opportunities within the entire spectrum of educational levels and majors. According to the UNESCO, since the onset of pandemic, more than 1.5 billion students worldwide (90.1% of total enrolled learners) have been affected by the COVID-19 closures and subsequent educational changes [ 8 ]. The sudden closure of most educational institutions around the world compelled the conversion of the face-to-face instruction into a fully online (or blended/hybrid) format in a short transitional time. As a result, academic institutions that were mainly focused on traditional face-to-face instructions encountered various challenges in this transition [ 9 ].

Urgent, careful and evidence-based planning is needed to mitigate the impact of pandemic on engineering education especially for vulnerable, disadvantaged and underrepresented students facing substantial challenges beyond their academic responsibilities, including family obligations, financial burden and additional employments [ 10 – 12 ]. Additional efforts need to be taken to guarantee that the online instruction of engineering courses still meets the rigorous requirements of the program accreditations such as Accreditation Board for Engineering and Technology (ABET).

Despite the existing literature on online engineering education, to the best of our knowledge, there has been no thorough (quantitative and qualitive) analysis of challenges and factors affecting the pandemic online engineering education in the universities that were mainly offering face-to-face classes pre-pandemic. This work is aimed for addressing this gap by considering the following two questions:

• What are the main challenges influencing online engineering education during COVID-19 pandemic for institutions that were mainly focused on traditional face-to-face instruction pre-COVID?
• What are the empirical insight and recommendations to address these challenges?

Sloan’s online learning consortium has defined the five pillars of high-quality online education as: learning effectiveness, student satisfaction, faculty satisfaction, access, scale, and cost [ 1 ]. Given these factors, we designed and conducted surveys among engineering faculty members and students at California State University, Long Beach (CSULB) to systematically investigate the challenges encountered during the abrupt transition from face-to-face to the online mode of instruction in Spring 2020. This paper presents the results of the conducted surveys and propose solutions for the improvement of online engineering education. Sharing the results of this observational study with other educators can facilitate a more robust continuity of engineering education during ongoing pandemic. It can also aid with overall improvement and consequently further promotion of online engineering education in the post-pandemic era especially for universities that were previously focused on traditional face-to-face instruction. CSULB is one of the most diverse universities in the U.S. in terms of race/ethnicity, gender, financial and cultural characteristics (e.g. with a large percentage of first-generation or low-income students). Thus, the results of this study can especially help the institutions with similar demographics to enhance their online engineering education during and post-pandemic.

1.1. Related work

The existing literature has identified several challenges that need to be considered for the effective design and offering of online courses:

• Converting a course from conventional face-to-face to the online format is time consuming and requires the instructor’s familiarity with (or willingness to learn about) online learning pedagogy and instructional tools, including the learning management system (LMS) [ 13 ].
• Some students prefer to learn difficult concepts face-to-face [ 14 ] and believe that face-to-face instructions provide deeper level of learning compared to the online [ 15 ].
• Designing a fair, equitable, and rigorous assessment to minimize cheating and plagiarism is difficult in online environment [ 16 ].
• A successful education requires creating and maintaining a reliable and robust infrastructure that supports both faculty and students [ 7 , 17 – 19 ].
• Hands-on training to work with equipment, instruments, and materials in a controlled laboratory setting is an inherent and necessary aspect of a successful engineering education [ 1 , 10 ]. Addressing this essential aspect within a fully online teaching platform is challenging particularly at the undergraduate level.

Recently, several studies have tried to identify the major factors and best practices that contribute to the acceptance, assimilation and success of online education including course design, course content support, instructor’s personal characteristics and students’ familiarity with and access to technical resources [ 20 – 22 ]. Due to sudden conversion to online instructions, caused be COVID-19, faculty and students at academic institutions, mainly focused on traditional face-to-face instruction, encountered various challenges. As the pandemic continues, a small body of literature on educational impact of COVID-19 is starting to emerge. A group of investigators conducted a U.S. nationwide survey study among faculty and students of STEM fields in June 2020. Their results highlighted the gender disparities in online learning during pandemic: female faculty and students reported more challenges in technological issues and adapting to remote learning compared with their male peers [ 12 ].They also found out that 35.5% of doctoral students, 18.0% of master’s students and 7.6% of undergraduate students would have a delayed graduation due to pandemic [ 11 ]. Hispanic and Black undergraduates were two times and 1.7 times more likely, respectively, to delay graduation relative to Whites.

Dhawan presented a comprehensive literature review on the existing pedagogical approaches for the online instruction while identifying the strengths, weaknesses and challenges of adopting each approach for the online education during the COVID-19 pandemic [ 9 ].

Vielma and Brey conducted a qualitative surveying from 170 students who took asynchronous classes within two engineering departments (biomedical engineering and chemical engineering) at a U.S. Hispanic-serving institution [ 10 ]. The goal was to assess the effectiveness of their online education during pandemic. Their results indicated the students’ need in having synchronous instructional content (in addition to asynchronous content) to enhance the social component of learning.

Almaiah et al. conducted a semi-structured interview (using a list of general topics as interview guideline instead of a structured list of questions) with 30 students and 31 experts in the field of information technology from six universities in Jordan and Saudi Arabia. Their goal was to identify the challenges that impede the successful employment of online education during pandemic in developing countries and provide educational stakeholders with useful guidelines to enhance education efficacy.

Our work conducts a thorough ( quantitative and qualitive ) analysis of challenges and factors affecting the online education of engineering courses by conducting surveys among students and faculty members from various engineering subfields at one of the largest and most diverse four-year U.S universities (CSULB). Thus, the presented work has several unique aspects that distinguish it from the few existing studies focused on online education during pandemic, such as the use of both quantitative and qualitative survey questions, and participation of large number of engineering students and faculty from various subfields and diverse backgrounds. Our observational study provides empirical evidence for various solutions we propose to enhance online engineering education during and post-pandemic, especially for those universities with limited resources, or with a large population of minority, first-generation and low-income students.

2. Materials and methods

2.1. engineering education at csulb.

California State University, Long Beach (CSULB) is one of the largest and most diverse four-year universities in the U.S. Approximately 52% of CSULB student body are NSF-defined underrepresented minority including 59.2% female, 46.9% Hispanic, 4.5% African American and 1% Native American [ 23 ]. As a result, CSULB is recognized as a minority serving institution: namely Hispanic, Asian American, Native American, and Pacific Islander-Serving Institution. Also, more than half of our students are low-income or first-generation college students. CSULB College of Engineering (COE) currently has more than 250 faculty and 5000 students (undergraduate and graduate). COE offers a total of 11 programs that are hosted by six departments: Biomedical Engineering (BME), Chemical Engineering (CHE), Civil Engineering & Construction Engineering Management (CECEM), Computer Engineering & Computer Science (CECS), Electrical Engineering (EE), and Mechanical & Aerospace Engineering (MAE). The majority of the courses in COE were offered face-to-face prior to pandemic. Since 2010, CSULB has been using an LMS called BeachBoard (BB) – a customized version of "Brightspace" platform developed and supported by "Desire 2 Learn" company. BB provides various features to facilitate the course instruction, including a robust platform for communication between the instructor and students, sharing course materials and recorded lectures with students, discussion forums, design and management of assessments, assignments and grades. Prior to pandemic, while some CSULB faculty members had been employing (at least some of) BB features (e.g. gradebook) for their instruction on a regular basis, many others had opted out as its usage has not been mandatory.

The unprecedented circumstances of global COVID-19 pandemic forced the swift conversion of the mode of instruction from face-to-face to fully online for all CSULB engineering programs (including 349 courses for a total of 1004 sections) within a transitional period of 10 days in March 2020. Hence, the teaching materials and assessment methods had to be developed “on the fly”. CSULB advised instructors to mainly focus on learning/using BB (and Zoom videoconferencing) to convert their instructions to the online format. This recommendation seemed reasonable given the availability and practicality of BB features. However, both our students and faculty encountered various challenges during the online instruction in Spring 2020. By the end of the semester in May 2020, CSULB announced that Fall 2020 semester was also going to be in the alternative mode of instructions. Thus, 313 engineering courses were scheduled to be offered in synchronous fully online format. 18 additional classes were exempted and offered in hybrid/blended format. These were the classes where the face-to-face component is considered essential to meet the course learning outcomes and therefore could not be conducted fully online, (e.g. laboratories and senior design capstone projects).

2.2. Surveys

Our goal was to identify and study the magnitude of various issues that our faculty and students encountered during the six weeks of online instruction in Spring 2020 (March 23-May 8) and plan for an enhanced online instruction in Fall 2020. The faculty and student surveys were designed holistically considering the overall verbal feedback received from stakeholders during the Spring 2020 online instruction. The faculty survey consisted of 10 multiple-choice and 2 free-response questions, while student survey included 8 multiple-choice questions with fill-in or additional comment options for each question.

The faculty survey questions covered a variety of online teaching issues including, but not limited to, the lack of access to necessary hardware (e.g. computer/tablet, stylus, scanner/printer, microphone/headset, camera), software and reliable internet connection. Some questions focused on various learning assessment methods that instructors used in Spring 2020 (or the ones they were planning to use in Fall 2020) including open-book or closed-book exams, synchronous or asynchronous exams, fully-online exam (using randomized questions on BB) or semi-online exams (where students solve the assigned problems on a paper, then scan and upload their solutions on BB). Some questions focused on proctoring exams and the instructors’ perceived prevalence of cheating/plagiarism. Faculty were also asked to indicate the topics that they were interested to enhance their skills on, e.g., basic or advanced BB features, Zoom features, automatic grading, etc. The two open-ended questions provided instructors additional opportunities to comment about their online teaching experience and make any suggestion or request to COE that could help with improvement of online instruction in Fall 2020.

The student survey was designed to identify the challenges students confronted during online instruction in Spring 2020, including lack of access to hardware, software, reliable internet connection, quiet/private space to study, potential issues of balancing study with work and family duties, and stress management. The students were also asked about the difficulties they had during the synchronous classes on Zoom (e.g., lack of focus or engagement, instructor’s lack of familiarity with technology) or during the online exams (e.g., time management, issues with methods of proctoring using camera). Copies of faculty and student surveys are enclosed in the S1 Appendix for the readers’ further reference.

The faculty survey was conducted using Qualtrics over a three-week period (June 20-July 10). Similarly, the student survey was designed and conducted in Qualtrics afterwards (July 27-August 12). This later timeframe was decided based on the assumption that more students (including the incoming students) might be available to participate in the survey closer to the beginning of the Fall 2020 semester (August 21). Participation in both surveys were anonymous.

A total of 110 instructors took the survey where 43% of them were full-time including tenured/tenure track faculties and the rest were part-time lecturers. Also, 627 students participated in the survey: First-year students (4%), Sophomore (14%), Junior (30%), Seniors (35%) and graduate students (17%). Fig 1 shows the distribution of survey participants among various departments within the COE (question #1 on both surveys). We observe that all departments have relatively similar representations in terms of percentage of faculty and student participants in respective surveys (9% BME, 5–10% CHE, 15–23% CECEM, 19–22% CECS, 18–22% EE, and 21–26% MAE).

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Distribution of the survey participants among various departments within the college of Engineering: (A) Faculty participants; (B) Student participants.

https://doi.org/10.1371/journal.pone.0250041.g001

These percentages are consistent with the size of our departments in terms of total number of faculty and students. Therefore, our survey sample population could be a good representative of the general COE populations in terms of existing majors.

3.1. Logistical challenges for both students and faculty

Fig 2 shows the percentages of survey respondents who indicated various logistical challenges they had during the online instruction period of Spring 2020 (question #3 on the faculty survey and question #3 on the student survey). Close to 15% of the faculty had issues with software license or no access to personal computer/tablet. About 20% of the faculty did not have access to microphone/headset or printer/scanner. 23% of faculty had no reliable internet connection, while 32% had no access to webcam or camera for the online instruction. Finally, 47% of the faculty indicated that they had no access to or had technical difficulties with online writing tools. Among the student respondents, 1% had no access to any computer/tablet, while close to 5% had only access to a shared computer at home. 3% had no internet connection, while 26% had issues with reliability of their internet. 28% indicated having issues with software access, while 26% had no printer/scanner at home.

The horizontal access represents the percentage of survey participants who indicated the corresponding challenge. (A) Faculty respondents; (B) Student respondents.

https://doi.org/10.1371/journal.pone.0250041.g002

3.2. Students challenges with online instruction

Fig 3 summarizes the prevalence of challenges students had with online instruction during Spring 2020 (questions # 3–6 on the student survey). About 70% of students indicated difficulty in maintaining their focus or experiencing Zoom fatigue after attending multiple online sessions. 55% of students felt social disconnection from their classmates/peers, while 64% did not feel engaged during the online classes. 60% of the students felt there was a lack of clear guidance or communication from the instructors. Also, a quarter of students had issues with online submission of assignments and exams, mainly due to the lack of access to printer/scanner as we learned from students’ optional comments. About 40% of students had technical difficulty and ineptness issues with using or navigating through Zoom or BB. 48% of the students experienced time management issues during the online exams. In optional comments, some students expressed their frustration with not being able to go back to previous questions (a BB feature for the instructors to limit cheating). 23% of the students indicated that the unavailability of the instructor during the online exam (in contrast to in-person exam) caused challenges.

https://doi.org/10.1371/journal.pone.0250041.g003

48% of the students specified that they either do not have camera or feel uncomfortable turning the camera/microphone on during the class or online exams (question #7 on the student survey). Optional comments revealed that many participants have privacy concerns with usage of camera/microphone or being recorded, especially if they were living in a crowded home or shared space. Furthermore, some students experienced an increased level of anxiety being watched on camera that hindered their focus and lowered their performance during the online exams. 28% of the students indicated that they had difficulty with balancing work and study. From the optional comments, we understood that the latter issue has been escalated for many during pandemic. Some parents had lost their jobs and consequently the whole family was relying on the part-time jobs of the younger adults (students) to survive financially.

Our survey also indicated that more than 50% of our students did not have access to a private or quiet space to attend the online classes or to study. 55% of students also lacked motivation to study (question #3 on the student survey). The optional comments shed further light onto the lack of motivation: the uncertainty of the COVID-19 pandemic and loss of peer interaction/support were identified as the major contributing factors. Finally, 24% of the students rated their overall experience of online instruction (question #8 on the student survey) as satisfying, 37% found it dissatisfying, while the rest (39%) were neutral.

3.3. Assessment methods used during emergency online instruction

Table 1 shows the prevalence of various methods that the faculty used to assess students’ learning during the online instruction of Spring 2020. Semi-online refers to an exam where students solve the assigned problems on a paper, then scan and upload their solutions. Asynchronous exam refers to a take-home exam while a synchronous exam is the one conducted during the scheduled class or exam time. The survey allowed respondents to choose more than one assessment method per question (because faculty might have taught multiple classes, held more than one exam during the semester or applied multiple assessment methods in the same class), thus the sum of the percentages would not equal to 100.

The respondents could choose more than one option for each question depending on the number of exams administered during the semester.

https://doi.org/10.1371/journal.pone.0250041.t001

We observe that the fully online exams such as the BB quizzes were used by 63% of the faculty. BB quizzes provides the faculty with the convenient option of randomizing the order and/or the parameter values of the questions. The instructor can also limit the view to one question per page for students and prevent them from going back to previous questions. The effectiveness of these options in limiting cheating/ plagiarism, and consequently the reduced need for further proctoring, might have contributed to the high popularity of this assessment method among the faculty.

The remaining assessment methods in the decreasing order of their prevalence were project/term paper (50%), semi-online synchronous exam (40%), oral presentation/exam (33%), and semi-online asynchronous exam (28%). Our survey also revealed that 70% of the faculty used the open-book/open-note exam while 33% tried closed-book/closed note exams. The preference of open-book/open-note exam among faculty could be also justified by the decreased need for proctoring tools. In fact, our data (faculty survey question #7) revealed that among those faculty who employed open-book/open-note exam, only 27% used Zoom camera and microphone for proctoring of the exam. 21% used lockdown browsers (e.g. respondus), while 61% did not have any proctoring. However, when the exams were closed-book/closed-note, 56% of the faculty decided to proctor the exam using Zoom camera and microphone, 18% chose to use the lockdown browsers and 35% did not proctor. We also evaluated the association of instructors’ perception of cheating/plagiarism with various assessment methods by calculating the Pearson correlation of faculty’s assessment methods with their trichotomized perception of online cheating (less cheating, the same, more cheating) relative to that of face-to-face (faculty survey question #9). The results revealed no statistically significant correlation between perception of cheating and assessment methods except for the following: Semi-online asynchronous exam (correlation = 0.23, p-value = 0.01) and Closed-note/Closed-book (correlation = 0.21, p-value = 0.03). This data analysis shows that semi-online asynchronous and closed-book exams were associated with an increase in the perceived cheating,

3.4. Perceived faculty skills that needed enhancement

Faculty indicated various topics that they were interested to enhance their skills in, as summarized in Table 2 .

Respondents could choose as many topics as they were interested to learn.

https://doi.org/10.1371/journal.pone.0250041.t002

About 60% of the faculty needed to learn about the advanced features of BB (e.g. how to create online surveys or make quizzes with randomized questions or personalized parameter values). Also, more than half of the faculty were interested in learning about semi-automatic grading tools (e.g. Gradescope). Close to 40% of the faculty needed to learn how to create a syllabus for an online class or become more competent with using Zoom features. A similar percentage of participants indicated interest in enhancing their multimedia skills (e.g. working with Kaltura Capture, Camtasia or Snagit). Finally, 26% of the faculty needed more training to become familiar with basic features of BB. In the optional comments (faculty survey questions #11–12), some faculty members expressed their concerns about the delivery of the hands-on components of their courses and requested some general guideline on how to address this issue for an online instruction.

4. Discussion

In this section, we will discuss the challenges we identified and propose relevant interventions to improve the online delivery of engineering courses during pandemic.

4.1. Student challenges

Our results revealed that a quarter of our students did not have access to reliable internet connection, triggering a concern about widening of the digital equity gap among students due to COVID-19 pandemic. With COVID-19 and the abrupt transition to online teaching, access to reliable internet connection and personal computer/tablet have become major factors affecting the learning outcomes for students. To address this issue, institution can provide WiFi access on campus’s open areas and well-ventilated buildings while monitoring for social distancing and sanitizing the surfaces frequently. For those who require computing devices, a loaner program can be implemented where students can borrow laptops for a certain period of time to access the course materials and complete the course requirements. The institution can also provide a virtual desktop environment for students to access all necessary software. Using free scanning applications on smartphones or tablets can address the lack of access to scanners.

Our survey also indicated that about 30% of engineering students had work-life balance issues, while 55% of students lacked motivation, and 50% did not have access to a private space to attend classes. These results are consistent with those reported in a recent study conducted at Biomedical and Chemical Engineering departments of a Hispanic-serving institution [ 10 ]. While the percentage of our students who had issues with lack of motivation or private space seemed to be higher, both studies highlight the necessity of providing more socio-emotional support for students during the difficult times of pandemic.

Students identified various challenges they experienced in online synchronous instruction of courses through Zoom including lack of peer-support/interaction, focus, engagement, and clear guideline from instructors. They also indicated difficulties with time management and Zoom fatigue. Peer-support/interaction has shown to improve the success rate of students especially those from underrepresented groups [ 24 ]. Lack of peer-support during the online instruction in the COVID-19 era negatively affects the motivation of the students. However, the remaining raised issues could be addressed in part by employing appropriate teaching techniques by faculty as follows: breaking down a long lecture into shorter segments with more frequent breaks, encouraging group discussion among students, making themselves available during the exams, providing students with a clear roadmap for the online course, making the recordings of the live lectures available after the lecture is over. The latest would help struggling students to learn at their own pace [ 10 ]. To assist with the time management issue during the exams, faculty can design practice exams to allow students to familiarize themselves with the questions’ setup and adapt with the exam’s style before the actual exam.

Pandemic has caused educational loss, delayed graduations, cancelled internships and lost job offers. The new generation of students who have been away from face-to-face instructions may lack certain learning experiences. For example, there might be a generation of engineering students who performed the majority of their lab activities virtually and thus, lacks true hands-on skills. While the pandemic educational gap will affect everyone, it is likely to impact under-privileged students (e.g. first generation, low income or care givers) more profoundly [ 25 ]. As a result, the socioeconomic factors would constitute key mediators in explaining the potentially large and heterogeneous educational gap. This gap may have long-lasting implications for income inequality and health disparities [ 26 ].

To reduce the educational gap, universities could adopt the practice of developing and implementing diagnostic tools to learn where and how large the deficiencies are. Based on the acquired knowledge, they could offer short remediation programs with long-term reorientation of curriculum to align with student’s learning levels [ 27 ]. For example, a summer session that deals with hands-on aspects of lab safety or experimentations could be implemented. In some cases, close coordination between the instructors who teach the courses in a sequence may be required, so they can develop extracurricular materials or propose activities that would help students bridge a gap in a specific topic. As the pandemic progresses, the flexibility of university policies could also help with narrowing down the educational gap especially for those students with lower socioeconomical status. Allowing students to adjust their course load, timing of assignments and tuition payment schedule would enable them to make reactive decisions to mitigate the educational loss [ 25 ]. A need for further research on this top is undeniable.

4.2. Faculty challenges

Establishment of institutional quality standards related to online education is of paramount importance in online education. Effective communication is the key factor in bridging the divide and reconciling administrator and faculty for an enhanced online education [ 28 ]. A considerable number of our faculty reported lack of access to hardware, software and necessary tools for online instruction. Especially, in the absence of traditional in-class whiteboard, many faculty members indicated lacking an online writing tool. This issue can be addressed by institution’s budget allocation to acquire necessary hardware and tools (e.g. personal computer/tablet with web camera, digital pen for touch screen devices, digital clipboard, document camera).

Development of online learning assessment methods as rigorous as in conventional face-to-face setting to prevent cheating/plagiarism is not straightforward [ 16 , 29 ]. While one cannot propose a single assessment method that would work ideally for all engineering courses and classroom sizes, it would still be interesting to study how various online exams and assessment methods (e.g. online quiz tools within the LMS, open-book or take-home examinations, student presentations, peer-reviewed activities, cooperative quizzes [ 30 ], oral assessments [ 31 ], course summary papers or online portfolios) stack up against each other. Since the onset of pandemic, a limited number of studies (mainly within the fields outside the engineering) have been conducted to evaluate the successes and challenges of the online assessments. The study in [ 32 ] revealed that although the majority of undergraduate Management students required more time and effort to prepare for the online exams (compared to the traditional exams), they regarded the clarity and prompt grading and feedback features of the online exams substantially advantageous. Another recent study revealed that cheating remains one of the major concerns for the online examinations and needs to be addressed using available techniques including online proctoring and randomizations of the exam questions [ 33 ]. Few other studies showed that the online examinations increased the level of stress and anxiety among medical students [ 34 , 35 ]. The added stress was in part caused by the lack of a robust examination platform (i.e., reliable LMS) as well as not providing students with sample online practice exams. Finally, a survey conducted among civil engineering students showed that high-achieving students performed significantly better than low-achieving students in online examinations and there was a significant increase in the students’ dropout rate in the 2020–2021 academic year relative to the previous ones [ 36 ].

Our student survey results indicated that the use of camera/microphone to proctor the online exams can raise equity concerns (for those who do not have access to camera and cannot afford it) and privacy concern (for monitoring students’ private space). To address these valid concerns, faculty are advised to choose alternative methods for reducing cheating during online exams. Randomizing the exam questions by shuffling both the problem statements and the multiple choices, and randomly selecting a subset of questions from a question library with individualized/randomized input variables are viable practical solutions. Fortunately, most LMS provide these options. However, although 99% of postsecondary US institutions have an LMS in use, only approximately half of faculty at those institutions have been using it on a regular basis [ 37 ]. As a result, many faculty members were not familiar with the basic or advances features of the LMS or other tools for effective online instruction. Our survey result confirmed this observation. In fact, our faculty identified a broad range of topics related to BB or other online teaching tools that they felt the need to enhance their skills in. Institutions could address this issue by organizing training workshops, webinars, short-courses, and discussion panels for the faculty to enhance their online teaching skills. At CSULB, stipends were offered in summer 2020 to further incentivize faculty participation in these professional development programs.

Hands-on training is an integral component of engineering education. Following the abrupt conversion of classes to the online format in Spring 2020, many instructors adopted simulations or processing of already acquired data for engineering students to complete their course projects. Our survey indicated the faculty’s need to learn about additional effective ways for providing hands-on training/experience. Depending on the content of the course, employment of “home lab kits” and recording of the lab experiments could partially help. However, design, preparation, distribution/collection of the lab kits or recording of the experiments can be extremely time consuming for faculty especially given all the access restrictions to on-campus labs and additional safety precautions imposed by COVID-19 pandemic. Virtual labs might be a more effective solution. Additionally, remotely accessible labs where the experiment setup is on campus and students use tools for remote control and managing of the setup can be employed, whenever possible [ 10 ].

4.3. Summary of proposed interventions

From the analysis of the survey results we propose several intervention strategies that can be employed by stakeholders at different levels to improve the online instruction of engineering courses. The proposed strategies (the targeted issues and the survey questions that identified them) are summarized as follows:

• Budget allocation to provide basic equipment for the online instruction to both faculty and students in need. Examples of such equipment include personal computer/tablet preferably with webcam/camera, online writing tool, reliable internet connection (to address the logistical challenges indicated by students and faculty in response to question # 3 of both surveys)
• Creating a virtual desktop environment and allowing faculty and students to access necessary software (addressing technical access challenges of online instruction indicated in response to questions # 3, #7 and # 11 from the faculty survey, and question #5 from the student survey)
• Organizing training workshops for faculty/students to further familiarize with online teaching/learning technology and tools (addressing technical skills that were indicated in response to question #10 of the faculty survey and question #5 of the student survey)
• Providing a syllabus template for online courses including all the important information needed for ABET accreditation (addressing lack of clear communication or instruction indicated in response to question #10 of the faculty survey and question #5 of the student survey)
• Development and organization of systematic repository of resources pertinent to engineering online instruction (to enhance the faculty’s online teaching skills as the need was indicated in response to questions #10–12 of the faculty survey)
• Leveraging on the institution’s LMS to manage the course, grades, forum discussions and exams (to enhance the faculty’s online teaching skills as the need was indicated in response to questions #10–12 of the faculty survey)
• Breaking down a long lecture into shorter segments with more frequent breaks (addressing Zoom fatigue indicated in response to question #4 of the student survey)
• Encouraging group discussion or problem-solving activities among students such as Zoom breakout rooms (addressing the lack of social interactions with peers as indicated in response to question # 4 of the student survey).
• Being available during the exams (e.g. on Zoom) to answer students’ questions (addressing the lack of access to the instructors during exams as indicated in response to question # 4 of the student survey).
• Providing students with a clear roadmap and instruction for the online course (addressing lack of clear communication or instruction indicated in response to question #5 of the student survey)
• Making the recordings of the live lectures available after the lecture (addressing online instruction challenges and lack of access to reliable internet indicated in response to question #4 and question #3 of the student surveys, respectively)
• Administering practice exams for students (addressing issues with the online exams indicated in response to question #6 of the student survey)
• Using open-book/open-note and synchronous assessment methods that support academic integrity. Examples include randomized questions/ restricted time/ question pools on LMS. (addressing the challenges with online assessment methods indicated in response to questions # 4, #7–9 of the faculty survey)
• Avoiding using camera/microphone to proctor exams (addressing privacy issues with the indicated in response to question #7 of the student survey)
• Employment of “home lab kits”, recording of the hands-on experiments and virtual labs to partially address the hands-on training aspect of the course (enhancing online instruction as indicated in response to questions # 11–12 of the faculty survey)
• Using free scanning applications on their smartphones (addressing lack of access to scanner as indicated in response to questions # 6 of the student survey).

Most of the proposed solutions were implemented at the CSULB college of Engineering in preparation for Fall 2020 semester. Our future work will include evaluation of the efficacy of the implemented interventions by conducting a post-intervention survey at the end of Spring 2021 semester.

This work contributes to the developing body of knowledge about the effect of pandemic on engineering education by investigating the challenges and obstacles faced by a large group of engineering students and faculty at CSULB which exemplifies an institution that previously taught face-to-face engineering classes (predominantly), with a large minority population and socio-economic gap. The recommended strategies for various educational stakeholders (including students, faculty and administration) aims to fill the tools and technology gap, enhance faculty skills in teaching online courses by taking full advantage of online learning management tools, and finally, propose effective assessment methods for online courses while considering the potential equity and privacy issues. These recommendations are practical approaches for many similar institutions around the world and would help improve the learning outcomes of online educations in all fields of engineering.

4.4. Potential limitations of the study

Some limitations should be addressed in this study. We investigated the challenges of engineering online education during Spring 2020 – when the pandemic started, and a global emergency occurred. Thus, the reported experiences and perceptions might have been affected by confounding factors related to the onset of pandemic. As the pandemic continues and various academic stakeholders explore and learn about new strategies to better adjust to the new normal , subsequent studies conducted in the near future might provide a more accurate picture of the online engineering education.

We advertised the surveys to all faculty and students of the CSULB college of Engineering by sending announcement emails to their university email accounts in summer 2020. While the faculty survey’s response rate was 44%, the student survey’s response rate was 12%. The low response rate of the students might have introduced some participation bias to the results.

Our main goal of conducting the surveys was to identify the urgent needs and challenges of the general body of our students and faculty without focusing on any specific underrepresented groups. Our assumption was that the demographics of survey participants are likely proportional to those of the college of Engineering. Further studies with inclusion of race, gender and socioeconomics demographics are needed to investigate the magnitude of educational challenges that underrepresented groups experienced during the pandemic in comparison with other groups. Consideration of some institutional data (e.g. grades, faculty/ student perception of learning, financial aid requests) from both pre- and during pandemic would enhance the study, as well.

The current work did not evaluate the degree of effectiveness and sustainability of each conducted intervention. It also did not compare the efficacy of various alternative assessment methods for engineering online education. A follow-up study is needed to address these limitations.

5. Conclusion

We conducted an observational study to identify challenges encountered due to abrupt transition to online instruction of engineering courses during COVID-19 pandemic by surveying (quantitively and qualitatively) students and faculty at our minority-serving institution. Various logistical, technical and learning/teaching issues were identified, and several interventions were proposed to address them. The results of this study add to the developing body of knowledge about the effect of pandemic on engineering education. This study also provides empirical evidence for the proposed strategies to enhance (and consequently further promote) the online engineering education during and post-pandemic. Our future work will include a thorough study on evaluating the efficacy and sustainability of each proposed intervention.

Supporting information

S1 appendix. questionnaire for both student and faculty surveys..

https://doi.org/10.1371/journal.pone.0250041.s001

S1 Data. Students survey data in response to multiple choice questions.

https://doi.org/10.1371/journal.pone.0250041.s002

S2 Data. Faculty survey data in response to multiple choice questions.

https://doi.org/10.1371/journal.pone.0250041.s003

Acknowledgments

The authors would like to thank Dr. Daniel Whisler, Dr. Shabnam Sodagari and Ms. Asieh Jalali-Farahani for their help with designing the surveys.

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Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19

• Published: 21 April 2021
• Volume 26 , pages 6923–6947, ( 2021 )

Cite this article

• Ram Gopal 1 ,
• Varsha Singh 1 &
• Arun Aggarwal   ORCID: orcid.org/0000-0003-3986-188X 2  

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The aim of the study is to identify the factors affecting students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19 and to establish the relationship between these variables. The study is quantitative in nature, and the data were collected from 544 respondents through online survey who were studying the business management (B.B.A or M.B.A) or hotel management courses in Indian universities. Structural equation modeling was used to analyze the proposed hypotheses. The results show that four independent factors used in the study viz. quality of instructor, course design, prompt feedback, and expectation of students positively impact students’ satisfaction and further student’s satisfaction positively impact students’ performance. For educational management, these four factors are essential to have a high level of satisfaction and performance for online courses. This study is being conducted during the epidemic period of COVID- 19 to check the effect of online teaching on students’ performance.

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• Digital Education and Educational Technology

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1 Introduction

Coronavirus is a group of viruses that is the main root of diseases like cough, cold, sneezing, fever, and some respiratory symptoms (WHO, 2019 ). Coronavirus is a contagious disease, which is spreading very fast amongst the human beings. COVID-19 is a new sprain which was originated in Wuhan, China, in December 2019. Coronavirus circulates in animals, but some of these viruses can transmit between animals and humans (Perlman & Mclntosh, 2020 ). As of March 282,020, according to the MoHFW, a total of 909 confirmed COVID-19 cases (862 Indians and 47 foreign nationals) had been reported in India (Centers for Disease Control and Prevention, 2020 ). Officially, no vaccine or medicine is evaluated to cure the spread of COVID-19 (Yu et al., 2020 ). The influence of the COVID-19 pandemic on the education system leads to schools and colleges’ widespread closures worldwide. On March 24, India declared a country-wide lockdown of schools and colleges (NDTV, 2020 ) for preventing the transmission of the coronavirus amongst the students (Bayham & Fenichel, 2020 ). School closures in response to the COVID-19 pandemic have shed light on several issues affecting access to education. COVID-19 is soaring due to which the huge number of children, adults, and youths cannot attend schools and colleges (UNESCO, 2020 ). Lah and Botelho ( 2012 ) contended that the effect of school closing on students’ performance is hazy.

Similarly, school closing may also affect students because of disruption of teacher and students’ networks, leading to poor performance. Bridge ( 2020 ) reported that schools and colleges are moving towards educational technologies for student learning to avoid a strain during the pandemic season. Hence, the present study’s objective is to develop and test a conceptual model of student’s satisfaction pertaining to online teaching during COVID-19, where both students and teachers have no other option than to use the online platform uninterrupted learning and teaching.

UNESCO recommends distance learning programs and open educational applications during school closure caused by COVID-19 so that schools and teachers use to teach their pupils and bound the interruption of education. Therefore, many institutes go for the online classes (Shehzadi et al., 2020 ).

As a versatile platform for learning and teaching processes, the E-learning framework has been increasingly used (Salloum & Shaalan, 2018 ). E-learning is defined as a new paradigm of online learning based on information technology (Moore et al., 2011 ). In contrast to traditional learning academics, educators, and other practitioners are eager to know how e-learning can produce better outcomes and academic achievements. Only by analyzing student satisfaction and their performance can the answer be sought.

Many comparative studies have been carried out to prove the point to explore whether face-to-face or traditional teaching methods are more productive or whether online or hybrid learning is better (Lockman & Schirmer, 2020 ; Pei & Wu, 2019 ; González-Gómez et al., 2016 ; González-Gómez et al., 2016 ). Results of the studies show that the students perform much better in online learning than in traditional learning. Henriksen et al. ( 2020 ) highlighted the problems faced by educators while shifting from offline to online mode of teaching. In the past, several research studies had been carried out on online learning to explore student satisfaction, acceptance of e-learning, distance learning success factors, and learning efficiency (Sher, 2009 ; Lee, 2014 ; Yen et al., 2018 ). However, scant amount of literature is available on the factors that affect the students’ satisfaction and performance in online classes during the pandemic of Covid-19 (Rajabalee & Santally, 2020 ). In the present study, the authors proposed that course design, quality of the instructor, prompt feedback, and students’ expectations are the four prominent determinants of learning outcome and satisfaction of the students during online classes (Lee, 2014 ).

The Course Design refers to curriculum knowledge, program organization, instructional goals, and course structure (Wright, 2003 ). If well planned, course design increasing the satisfaction of pupils with the system (Almaiah & Alyoussef, 2019 ). Mtebe and Raisamo ( 2014 ) proposed that effective course design will help in improving the performance through learners knowledge and skills (Khan & Yildiz, 2020 ; Mohammed et al., 2020 ). However, if the course is not designed effectively then it might lead to low usage of e-learning platforms by the teachers and students (Almaiah & Almulhem, 2018 ). On the other hand, if the course is designed effectively then it will lead to higher acceptance of e-learning system by the students and their performance also increases (Mtebe & Raisamo, 2014 ). Hence, to prepare these courses for online learning, many instructors who are teaching blended courses for the first time are likely to require a complete overhaul of their courses (Bersin, 2004 ; Ho et al., 2006 ).

The second-factor, Instructor Quality, plays an essential role in affecting the students’ satisfaction in online classes. Instructor quality refers to a professional who understands the students’ educational needs, has unique teaching skills, and understands how to meet the students’ learning needs (Luekens et al., 2004 ). Marsh ( 1987 ) developed five instruments for measuring the instructor’s quality, in which the main method was Students’ Evaluation of Educational Quality (SEEQ), which delineated the instructor’s quality. SEEQ is considered one of the methods most commonly used and embraced unanimously (Grammatikopoulos et al., 2014 ). SEEQ was a very useful method of feedback by students to measure the instructor’s quality (Marsh, 1987 ).

The third factor that improves the student’s satisfaction level is prompt feedback (Kinicki et al., 2004 ). Feedback is defined as information given by lecturers and tutors about the performance of students. Within this context, feedback is a “consequence of performance” (Hattie & Timperley, 2007 , p. 81). In education, “prompt feedback can be described as knowing what you know and what you do not related to learning” (Simsek et al., 2017 , p.334). Christensen ( 2014 ) studied linking feedback to performance and introduced the positivity ratio concept, which is a mechanism that plays an important role in finding out the performance through feedback. It has been found that prompt feedback helps in developing a strong linkage between faculty and students which ultimately leads to better learning outcomes (Simsek et al., 2017 ; Chang, 2011 ).

The fourth factor is students’ expectation . Appleton-Knapp and Krentler ( 2006 ) measured the impact of student’s expectations on their performance. They pin pointed that the student expectation is important. When the expectations of the students are achieved then it lead to the higher satisfaction level of the student (Bates & Kaye, 2014 ). These findings were backed by previous research model “Student Satisfaction Index Model” (Zhang et al., 2008 ). However, when the expectations are students is not fulfilled then it might lead to lower leaning and satisfaction with the course. Student satisfaction is defined as students’ ability to compare the desired benefit with the observed effect of a particular product or service (Budur et al., 2019 ). Students’ whose grade expectation is high will show high satisfaction instead of those facing lower grade expectations.

The scrutiny of the literature show that although different researchers have examined the factors affecting student satisfaction but none of the study has examined the effect of course design, quality of the instructor, prompt feedback, and students’ expectations on students’ satisfaction with online classes during the pandemic period of Covid-19. Therefore, this study tries to explore the factors that affect students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19. As the pandemic compelled educational institutions to move online with which they were not acquainted, including teachers and learners. The students were not mentally prepared for such a shift. Therefore, this research will be examined to understand what factors affect students and how students perceived these changes which are reflected through their satisfaction level.

This paper is structured as follows: The second section provides a description of theoretical framework and the linkage among different research variables and accordingly different research hypotheses were framed. The third section deals with the research methodology of the paper as per APA guideline. The outcomes and corresponding results of the empirical analysis are then discussed. Lastly, the paper concludes with a discussion and proposes implications for future studies.

2 Theoretical framework

Achievement goal theory (AGT) is commonly used to understand the student’s performance, and it is proposed by four scholars Carole Ames, Carol Dweck, Martin Maehr, and John Nicholls in the late 1970s (Elliot, 2005 ). Elliott & Dweck ( 1988 , p11) define that “an achievement goal involves a program of cognitive processes that have cognitive, affective and behavioral consequence”. This theory suggests that students’ motivation and achievement-related behaviors can be easily understood by the purpose and the reasons they adopted while they are engaged in the learning activities (Dweck & Leggett, 1988 ; Ames, 1992 ; Urdan, 1997 ). Some of the studies believe that there are four approaches to achieve a goal, i.e., mastery-approach, mastery avoidance, performance approach, and performance-avoidance (Pintrich, 1999 ; Elliot & McGregor, 2001 ; Schwinger & Stiensmeier-Pelster, 2011 , Hansen & Ringdal, 2018 ; Mouratidis et al., 2018 ). The environment also affects the performance of students (Ames & Archer, 1988 ). Traditionally, classroom teaching is an effective method to achieve the goal (Ames & Archer, 1988 ; Ames, 1992 ; Clayton et al., 2010 ) however in the modern era, the internet-based teaching is also one of the effective tools to deliver lectures, and web-based applications are becoming modern classrooms (Azlan et al., 2020 ). Hence, following section discuss about the relationship between different independent variables and dependent variables (Fig. 1 ).

Proposed Model

3 Hypotheses development

3.1 quality of the instructor and satisfaction of the students.

Quality of instructor with high fanaticism on student’s learning has a positive impact on their satisfaction. Quality of instructor is one of the most critical measures for student satisfaction, leading to the education process’s outcome (Munteanu et al., 2010 ; Arambewela & Hall, 2009 ; Ramsden, 1991 ). Suppose the teacher delivers the course effectively and influence the students to do better in their studies. In that case, this process leads to student satisfaction and enhances the learning process (Ladyshewsky, 2013 ). Furthermore, understanding the need of learner by the instructor also ensures student satisfaction (Kauffman, 2015 ). Hence the hypothesis that the quality of instructor significantly affects the satisfaction of the students was included in this study.

H1: The quality of the instructor positively affects the satisfaction of the students.

3.2 Course design and satisfaction of students

The course’s technological design is highly persuading the students’ learning and satisfaction through their course expectations (Liaw, 2008 ; Lin et al., 2008 ). Active course design indicates the students’ effective outcomes compared to the traditional design (Black & Kassaye, 2014 ). Learning style is essential for effective course design (Wooldridge, 1995 ). While creating an online course design, it is essential to keep in mind that we generate an experience for students with different learning styles. Similarly, (Jenkins, 2015 ) highlighted that the course design attributes could be developed and employed to enhance student success. Hence the hypothesis that the course design significantly affects students’ satisfaction was included in this study.

H2: Course design positively affects the satisfaction of students.

3.3 Prompt feedback and satisfaction of students

The emphasis in this study is to understand the influence of prompt feedback on satisfaction. Feedback gives the information about the students’ effective performance (Chang, 2011 ; Grebennikov & Shah, 2013 ; Simsek et al., 2017 ). Prompt feedback enhances student learning experience (Brownlee et al., 2009 ) and boosts satisfaction (O'donovan, 2017 ). Prompt feedback is the self-evaluation tool for the students (Rogers, 1992 ) by which they can improve their performance. Eraut ( 2006 ) highlighted the impact of feedback on future practice and student learning development. Good feedback practice is beneficial for student learning and teachers to improve students’ learning experience (Yorke, 2003 ). Hence the hypothesis that prompt feedback significantly affects satisfaction was included in this study.

H3: Prompt feedback of the students positively affects the satisfaction.

3.4 Expectations and satisfaction of students

Expectation is a crucial factor that directly influences the satisfaction of the student. Expectation Disconfirmation Theory (EDT) (Oliver, 1980 ) was utilized to determine the level of satisfaction based on their expectations (Schwarz & Zhu, 2015 ). Student’s expectation is the best way to improve their satisfaction (Brown et al., 2014 ). It is possible to recognize student expectations to progress satisfaction level (ICSB, 2015 ). Finally, the positive approach used in many online learning classes has been shown to place a high expectation on learners (Gold, 2011 ) and has led to successful outcomes. Hence the hypothesis that expectations of the student significantly affect the satisfaction was included in this study.

H4: Expectations of the students positively affects the satisfaction.

3.5 Satisfaction and performance of the students

Zeithaml ( 1988 ) describes that satisfaction is the outcome result of the performance of any educational institute. According to Kotler and Clarke ( 1986 ), satisfaction is the desired outcome of any aim that amuses any individual’s admiration. Quality interactions between instructor and students lead to student satisfaction (Malik et al., 2010 ; Martínez-Argüelles et al., 2016 ). Teaching quality and course material enhances the student satisfaction by successful outcomes (Sanderson, 1995 ). Satisfaction relates to the student performance in terms of motivation, learning, assurance, and retention (Biner et al., 1996 ). Mensink and King ( 2020 ) described that performance is the conclusion of student-teacher efforts, and it shows the interest of students in the studies. The critical element in education is students’ academic performance (Rono, 2013 ). Therefore, it is considered as center pole, and the entire education system rotates around the student’s performance. Narad and Abdullah ( 2016 ) concluded that the students’ academic performance determines academic institutions’ success and failure.

Singh et al. ( 2016 ) asserted that the student academic performance directly influences the country’s socio-economic development. Farooq et al. ( 2011 ) highlights the students’ academic performance is the primary concern of all faculties. Additionally, the main foundation of knowledge gaining and improvement of skills is student’s academic performance. According to Narad and Abdullah ( 2016 ), regular evaluation or examinations is essential over a specific period of time in assessing students’ academic performance for better outcomes. Hence the hypothesis that satisfaction significantly affects the performance of the students was included in this study.

H5: Students’ satisfaction positively affects the performance of the students.

3.6 Satisfaction as mediator

Sibanda et al. ( 2015 ) applied the goal theory to examine the factors persuading students’ academic performance that enlightens students’ significance connected to their satisfaction and academic achievement. According to this theory, students perform well if they know about factors that impact on their performance. Regarding the above variables, institutional factors that influence student satisfaction through performance include course design and quality of the instructor (DeBourgh, 2003 ; Lado et al., 2003 ), prompt feedback, and expectation (Fredericksen et al., 2000 ). Hence the hypothesis that quality of the instructor, course design, prompts feedback, and student expectations significantly affect the students’ performance through satisfaction was included in this study.

H6: Quality of the instructor, course design, prompt feedback, and student’ expectations affect the students’ performance through satisfaction.

H6a: Students’ satisfaction mediates the relationship between quality of the instructor and student’s performance.

H6b: Students’ satisfaction mediates the relationship between course design and student’s performance.

H6c: Students’ satisfaction mediates the relationship between prompt feedback and student’s performance.

H6d: Students’ satisfaction mediates the relationship between student’ expectations and student’s performance.

4.1 Participants

In this cross-sectional study, the data were collected from 544 respondents who were studying the management (B.B.A or M.B.A) and hotel management courses. The purposive sampling technique was used to collect the data. Descriptive statistics shows that 48.35% of the respondents were either MBA or BBA and rests of the respondents were hotel management students. The percentages of male students were (71%) and female students were (29%). The percentage of male students is almost double in comparison to females. The ages of the students varied from 18 to 35. The dominant group was those aged from 18 to 22, and which was the under graduation student group and their ratio was (94%), and another set of students were from the post-graduation course, which was (6%) only.

4.2 Materials

The research instrument consists of two sections. The first section is related to demographical variables such as discipline, gender, age group, and education level (under-graduate or post-graduate). The second section measures the six factors viz. instructor’s quality, course design, prompt feedback, student expectations, satisfaction, and performance. These attributes were taken from previous studies (Yin & Wang, 2015 ; Bangert, 2004 ; Chickering & Gamson, 1987 ; Wilson et al., 1997 ). The “instructor quality” was measured through the scale developed by Bangert ( 2004 ). The scale consists of seven items. The “course design” and “prompt feedback” items were adapted from the research work of Bangert ( 2004 ). The “course design” scale consists of six items. The “prompt feedback” scale consists of five items. The “students’ expectation” scale consists of five items. Four items were adapted from Bangert, 2004 and one item was taken from Wilson et al. ( 1997 ). Students’ satisfaction was measure with six items taken from Bangert ( 2004 ); Wilson et al. ( 1997 ); Yin and Wang ( 2015 ). The “students’ performance” was measured through the scale developed by Wilson et al. ( 1997 ). The scale consists of six items. These variables were accessed on a five-point likert scale, ranging from 1(strongly disagree) to 5(strongly agree). Only the students from India have taken part in the survey. A total of thirty-four questions were asked in the study to check the effect of the first four variables on students’ satisfaction and performance. For full details of the questionnaire, kindly refer Appendix Tables 6 .

The study used a descriptive research design. The factors “instructor quality, course design, prompt feedback and students’ expectation” were independent variables. The students’ satisfaction was mediator and students’ performance was the dependent variable in the current study.

4.4 Procedure

In this cross-sectional research the respondents were selected through judgment sampling. They were informed about the objective of the study and information gathering process. They were assured about the confidentiality of the data and no incentive was given to then for participating in this study. The information utilizes for this study was gathered through an online survey. The questionnaire was built through Google forms, and then it was circulated through the mails. Students’ were also asked to write the name of their college, and fifteen colleges across India have taken part to fill the data. The data were collected in the pandemic period of COVID-19 during the total lockdown in India. This was the best time to collect the data related to the current research topic because all the colleges across India were involved in online classes. Therefore, students have enough time to understand the instrument and respondent to the questionnaire in an effective manner. A total of 615 questionnaires were circulated, out of which the students returned 574. Thirty responses were not included due to the unengaged responses. Finally, 544 questionnaires were utilized in the present investigation. Male and female students both have taken part to fill the survey, different age groups, and various courses, i.e., under graduation and post-graduation students of management and hotel management students were the part of the sample.

5.1 Exploratory factor analysis (EFA)

To analyze the data, SPSS and AMOS software were used. First, to extract the distinct factors, an exploratory factor analysis (EFA) was performed using VARIMAX rotation on a sample of 544. Results of the exploratory analysis rendered six distinct factors. Factor one was named as the quality of instructor, and some of the items were “The instructor communicated effectively”, “The instructor was enthusiastic about online teaching” and “The instructor was concerned about student learning” etc. Factor two was labeled as course design, and the items were “The course was well organized”, “The course was designed to allow assignments to be completed across different learning environments.” and “The instructor facilitated the course effectively” etc. Factor three was labeled as prompt feedback of students, and some of the items were “The instructor responded promptly to my questions about the use of Webinar”, “The instructor responded promptly to my questions about general course requirements” etc. The fourth factor was Student’s Expectations, and the items were “The instructor provided models that clearly communicated expectations for weekly group assignments”, “The instructor used good examples to explain statistical concepts” etc. The fifth factor was students’ satisfaction, and the items were “The online classes were valuable”, “Overall, I am satisfied with the quality of this course” etc. The sixth factor was performance of the student, and the items were “The online classes has sharpened my analytic skills”, “Online classes really tries to get the best out of all its students” etc. These six factors explained 67.784% of the total variance. To validate the factors extracted through EFA, the researcher performed confirmatory factor analysis (CFA) through AMOS. Finally, structural equation modeling (SEM) was used to test the hypothesized relationships.

5.2 Measurement model

The results of Table 1 summarize the findings of EFA and CFA. Results of the table showed that EFA renders six distinct factors, and CFA validated these factors. Table 2 shows that the proposed measurement model achieved good convergent validity (Aggarwal et al., 2018a , b ). Results of the confirmatory factor analysis showed that the values of standardized factor loadings were statistically significant at the 0.05 level. Further, the results of the measurement model also showed acceptable model fit indices such that CMIN = 710.709; df = 480; CMIN/df = 1.481 p  < .000; Incremental Fit Index (IFI) = 0.979; Tucker-Lewis Index (TLI) = 0.976; Goodness of Fit index (GFI) = 0.928; Adjusted Goodness of Fit Index (AGFI) = 0.916; Comparative Fit Index (CFI) = 0.978; Root Mean Square Residual (RMR) = 0.042; Root Mean Squared Error of Approximation (RMSEA) = 0.030 is satisfactory.

The Average Variance Explained (AVE) according to the acceptable index should be higher than the value of squared correlations between the latent variables and all other variables. The discriminant validity is confirmed (Table 2 ) as the value of AVE’s square root is greater than the inter-construct correlations coefficient (Hair et al., 2006 ). Additionally, the discriminant validity existed when there was a low correlation between each variable measurement indicator with all other variables except with the one with which it must be theoretically associated (Aggarwal et al., 2018a , b ; Aggarwal et al., 2020 ). The results of Table 2 show that the measurement model achieved good discriminate validity.

5.3 Structural model

To test the proposed hypothesis, the researcher used the structural equation modeling technique. This is a multivariate statistical analysis technique, and it includes the amalgamation of factor analysis and multiple regression analysis. It is used to analyze the structural relationship between measured variables and latent constructs.

Table  3 represents the structural model’s model fitness indices where all variables put together when CMIN/DF is 2.479, and all the model fit values are within the particular range. That means the model has attained a good model fit. Furthermore, other fit indices as GFI = .982 and AGFI = 0.956 be all so supportive (Schumacker & Lomax, 1996 ; Marsh & Grayson, 1995 ; Kline, 2005 ).

Hence, the model fitted the data successfully. All co-variances among the variables and regression weights were statistically significant ( p  < 0.001).

Table 4 represents the relationship between exogenous, mediator and endogenous variables viz—quality of instructor, prompt feedback, course design, students’ expectation, students’ satisfaction and students’ performance. The first four factors have a positive relationship with satisfaction, which further leads to students’ performance positively. Results show that the instructor’s quality has a positive relationship with the satisfaction of students for online classes (SE = 0.706, t-value = 24.196; p  < 0.05). Hence, H1 was supported. The second factor is course design, which has a positive relationship with students’ satisfaction of students (SE = 0.064, t-value = 2.395; p < 0.05). Hence, H2 was supported. The third factor is Prompt feedback, and results show that feedback has a positive relationship with the satisfaction of the students (SE = 0.067, t-value = 2.520; p < 0.05). Hence, H3 was supported. The fourth factor is students’ expectations. The results show a positive relationship between students’ expectation and students’ satisfaction with online classes (SE = 0.149, t-value = 5.127; p < 0.05). Hence, H4 was supported. The results of SEM show that out of quality of instructor, prompt feedback, course design, and students’ expectation, the most influencing factor that affect the students’ satisfaction was instructor’s quality (SE = 0.706) followed by students’ expectation (SE =5.127), prompt feedback (SE = 2.520). The factor that least affects the students’ satisfaction was course design (2.395). The results of Table 4 finally depicts that students’ satisfaction has positive effect on students’ performance ((SE = 0.186, t-value = 2.800; p < 0.05). Hence H5 was supported.

Table 5 shows that students’ satisfaction partially mediates the positive relationship between the instructor’s quality and student performance. Hence, H6(a) was supported. Further, the mediation analysis results showed that satisfaction again partially mediates the positive relationship between course design and student’s performance. Hence, H6(b) was supported However, the mediation analysis results showed that satisfaction fully mediates the positive relationship between prompt feedback and student performance. Hence, H6(c) was supported. Finally, the results of the Table 5 showed that satisfaction partially mediates the positive relationship between expectations of the students and student’s performance. Hence, H6(d) was supported.

6 Discussion

In the present study, the authors evaluated the different factors directly linked with students’ satisfaction and performance with online classes during Covid-19. Due to the pandemic situation globally, all the colleges and universities were shifted to online mode by their respective governments. No one has the information that how long this pandemic will remain, and hence the teaching method was shifted to online mode. Even though some of the educators were not tech-savvy, they updated themselves to battle the unexpected circumstance (Pillai et al., 2021 ). The present study results will help the educators increase the student’s satisfaction and performance in online classes. The current research assists educators in understanding the different factors that are required for online teaching.

Comparing the current research with past studies, the past studies have examined the factors affecting the student’s satisfaction in the conventional schooling framework. However, the present study was conducted during India’s lockdown period to identify the prominent factors that derive the student’s satisfaction with online classes. The study also explored the direct linkage between student’s satisfaction and their performance. The present study’s findings indicated that instructor’s quality is the most prominent factor that affects the student’s satisfaction during online classes. This means that the instructor needs to be very efficient during the lectures. He needs to understand students’ psychology to deliver the course content prominently. If the teacher can deliver the course content properly, it affects the student’s satisfaction and performance. The teachers’ perspective is critical because their enthusiasm leads to a better online learning process quality.

The present study highlighted that the second most prominent factor affecting students’ satisfaction during online classes is the student’s expectations. Students might have some expectations during the classes. If the instructor understands that expectation and customizes his/her course design following the student’s expectations, then it is expected that the students will perform better in the examinations. The third factor that affects the student’s satisfaction is feedback. After delivering the course, appropriate feedback should be taken by the instructors to plan future courses. It also helps to make the future strategies (Tawafak et al., 2019 ). There must be a proper feedback system for improvement because feedback is the course content’s real image. The last factor that affects the student’s satisfaction is design. The course content needs to be designed in an effective manner so that students should easily understand it. If the instructor plans the course, so the students understand the content without any problems it effectively leads to satisfaction, and the student can perform better in the exams. In some situations, the course content is difficult to deliver in online teaching like the practical part i.e. recipes of dishes or practical demonstration in the lab. In such a situation, the instructor needs to be more creative in designing and delivering the course content so that it positively impacts the students’ overall satisfaction with online classes.

Overall, the students agreed that online teaching was valuable for them even though the online mode of classes was the first experience during the pandemic period of Covid-19 (Agarwal & Kaushik, 2020 ; Rajabalee & Santally, 2020 ). Some of the previous studies suggest that the technology-supported courses have a positive relationship with students’ performance (Cho & Schelzer, 2000 ; Harasim, 2000 ; Sigala, 2002 ). On the other hand, the demographic characteristic also plays a vital role in understanding the online course performance. According to APA Work Group of the Board of Educational Affairs ( 1997 ), the learner-centered principles suggest that students must be willing to invest the time required to complete individual course assignments. Online instructors must be enthusiastic about developing genuine instructional resources that actively connect learners and encourage them toward proficient performances. For better performance in studies, both teachers and students have equal responsibility. When the learner faces any problem to understand the concepts, he needs to make inquiries for the instructor’s solutions (Bangert, 2004 ). Thus, we can conclude that “instructor quality, student’s expectation, prompt feedback, and effective course design” significantly impact students’ online learning process.

7 Implications of the study

The results of this study have numerous significant practical implications for educators, students and researchers. It also contributes to the literature by demonstrating that multiple factors are responsible for student satisfaction and performance in the context of online classes during the period of the COVID-19 pandemic. This study was different from the previous studies (Baber, 2020 ; Ikhsan et al., 2019 ; Eom & Ashill, 2016 ). None of the studies had examined the effect of students’ satisfaction on their perceived academic performance. The previous empirical findings have highlighted the importance of examining the factors affecting student satisfaction (Maqableh & Jaradat, 2021 ; Yunusa & Umar, 2021 ). Still, none of the studies has examined the effect of course design, quality of instructor, prompt feedback, and students’ expectations on students’ satisfaction all together with online classes during the pandemic period. The present study tries to fill this research gap.

The first essential contribution of this study was the instructor’s facilitating role, and the competence he/she possesses affects the level of satisfaction of the students (Gray & DiLoreto, 2016 ). There was an extra obligation for instructors who taught online courses during the pandemic. They would have to adapt to a changing climate, polish their technical skills throughout the process, and foster new students’ technical knowledge in this environment. The present study’s findings indicate that instructor quality is a significant determinant of student satisfaction during online classes amid a pandemic. In higher education, the teacher’s standard referred to the instructor’s specific individual characteristics before entering the class (Darling-Hammond, 2010 ). These attributes include factors such as instructor content knowledge, pedagogical knowledge, inclination, and experience. More significantly, at that level, the amount of understanding could be given by those who have a significant amount of technical expertise in the areas they are teaching (Martin, 2021 ). Secondly, the present study results contribute to the profession of education by illustrating a realistic approach that can be used to recognize students’ expectations in their class effectively. The primary expectation of most students before joining a university is employment. Instructors have agreed that they should do more to fulfill students’ employment expectations (Gorgodze et al., 2020 ). The instructor can then use that to balance expectations to improve student satisfaction. Study results can be used to continually improve and build courses, as well as to make policy decisions to improve education programs. Thirdly, from result outcomes, online course design and instructors will delve deeper into how to structure online courses more efficiently, including design features that minimize adversely and maximize optimistic emotion, contributing to greater student satisfaction (Martin et al., 2018 ). The findings suggest that the course design has a substantial positive influence on the online class’s student performance. The findings indicate that the course design of online classes need to provide essential details like course content, educational goals, course structure, and course output in a consistent manner so that students would find the e-learning system beneficial for them; this situation will enable students to use the system and that leads to student performance (Almaiah & Alyoussef, 2019 ). Lastly, the results indicate that instructors respond to questions promptly and provide timely feedback on assignments to facilitate techniques that help students in online courses improve instructor participation, instructor interaction, understanding, and participation (Martin et al., 2018 ). Feedback can be beneficial for students to focus on the performance that enhances their learning.

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Gopal, R., Singh, V. & Aggarwal, A. Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Educ Inf Technol 26 , 6923–6947 (2021). https://doi.org/10.1007/s10639-021-10523-1

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Adolescents’ physical activity during and beyond the Covid-19 pandemic: a qualitative study exploring the experiences of young people living in the context of socioeconomic deprivation

• Olivia Alliott 1 ,
• Hannah Fairbrother 2 &
• Esther van Sluijs 1  

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Adolescent physical activity levels are low and are shown to decline with age into adulthood. Emerging literature suggests these trends were exacerbated during the Covid-19 pandemic. We aimed to understand, from the perspective of adolescents living in deprived communities, whether the Covid-19 pandemic influenced their physical behaviour and explore their ideas for physical activity promotion moving forward.

Purposive sampling was used to recruit older adolescents (13-18-year-old) living in one of the 20% most deprived areas in the UK, as defined by the UK Index of Multiple Deprivation. A mix of in-person and online one-to-one semi-structured interviews were conducted between July 2021- March 2022. Interviews were audio-recorded, transcribed verbatim and anonymised. Data were imported into Nvivo software and analysed drawing on Braun and Clarke’s six phases of thematic analysis.

The sample consisted of 16 adolescents and included a mix of genders. The following themes were generated during the data analysis: (1) Physical activity behaviour in everyday life (prepandemic), (2) The impact of Covid-19 on physical activity (during) and (3) Young people’s ideas about physical activity promotion (moving forward). Participants described themselves as inactive, with their activity limited to active travel, informal activity and physical education. Experiences of the pandemic were largely negative, impacting participants’ physical and mental health. Ideas around physical activity promotion ranged from the individual to the societal level.

Conclusions

Our findings suggest the Covid-19 pandemic had a major impact on young people living in the context of socioeconomic deprivation. Physical activity promotion efforts should focus on school-based opportunities and the provision of safe and low-cost opportunities in socioeconomically deprived areas. As we aim to build back from the Covid-19 pandemic, supporting young people living in socioeconomically deprived communities should be prioritised.

Peer Review reports

The health benefits of physical activity throughout the life course are well documented [ 1 , 2 ]. Globally, the physical activity levels of 11-to-17-year-olds are low, with less than one in 10 adolescents meeting the physical activity guidelines of 60 min per day [ 3 , 4 ]. These levels decline with age into adulthood, highlighting the importance of continued public heath efforts to increase and maintain physical activity during this life stage [ 5 , 6 ].

Emerging literature suggests these trends were exacerbated during the Covid-19 pandemic. There is clear evidence for a decline in young people’s physical activity during the Covid-19 lockdowns [ 7 ]. The results of a recent systematic review and meta-analysis report a 17-minute reduction in young peoples’ daily moderate-to-vigorous physical activity levels during the Covid-19 pandemic [ 2 ]. Further evidence shows this decline was more prominent in older children and adolescents [ 3 , 8 ]. Across global literature, this has been attributed to the closure of schools, sports clubs, and fitness centres and restrictions preventing the running of organised recreational activities [ 9 , 10 ].

Globally, reports highlight inequalities experienced during the pandemic and its resulting restrictions [ 11 ]. This is attributed to the “syndemic” nature of pandemics, exacerbating existing inequalities in the social determinants of health [ 11 , 12 ]. Regarding young people’s physical activity, socioeconomic background is reported to have impacted activity levels during Covid-19, with review level evidence highlighting those of a higher socioeconomic position to have been more active [ 7 ]. Socioeconomic and environmental factors likely contributed to disparities in health behaviours, including physical activity. For instance, areas of high deprivation typically have fewer recreational spaces and poorer neighbourhood safety so residents need to travel longer distances to activity areas [ 13 , 14 , 15 ]. People living in deprived areas were further disadvantaged by travel restrictions during the pandemic meaning that they could not travel to facilities outside their areas. Moreover, confined living arrangements for households on low incomes reduced opportunities for home-based physical activity [ 16 ].

Whilst the global literature provides insight on the impact of the pandemic, this varies depending on national strategies to curb the spread of the virus and a country’s societal circumstances entering the pandemic. Within the United Kingdom (UK), Impact Inquiry reports suggest young people were one of the groups worst affected by pandemic restrictions, which placed a huge strain on their health and well-being [ 17 , 18 ]. Data from Sport England’s Active Lives Survey (2020–2021) aligns with global reports of a decrease in young people’s physical activity during the pandemic [ 19 ]. However, socioeconomic differences in physical activity remained similar to pre-pandemic values, suggesting inequalities remained constant.

Due to the nature of conducting research during a pandemic and under social distancing restrictions, conclusions have relied on self-reported survey data which have the potential to disguise socioeconomic inequalites [ 7 , 20 , 21 ]. Whilst this data show that young people from the UK across the socioeconomic spectrum became less active, previous research suggest those living in socioeconomically deprived communities likely had differing experiences and faced more barriers to being active [ 13 ]. However, this remains unclear in the UK context. As we recover from the pandemic, investment in equitable physical activity promotion is needed to mitigate the impact on health and health inequalities [ 22 ]. Developing our understanding of this from an adolescent perspective will help us learn from the pandemic and progress equitably toward national physical activity targets.

To achieves this, this study aims to understand, from the perspective of adolescents living in socioeconomically deprived communities, if and how the Covid-19 pandemic influenced their physical activity behaviour and explore their ideas for physical activity promotion moving forward.

A qualitative approach was chosen to facilitate an in-depth exploration of the perspectives and experiences of young people living in the context of socioeconomic deprivation [ 20 ]. The Socio-Ecological Model was employed for the design and analysis of this study, acknowledging the complexity of physical activity behaviour and the multiple levels of influence [ 23 ]. Grounded in a subtle realist philosophy, the researchers aimed to assess the social world through the participants’ interpretations while recognising the influence of their subjective perceptions and applied research methods [ 24 , 25 ].

This study was conducted following the Standards for Reporting in Qualitative Research (SRQR) (see Supplementary File 1 ) [ 26 ]. Ethical approval for the study was granted by the University of Cambridge School of Humanities and Social Sciences ethics committee (21.273).

Participants and recruitment

Purposive sampling was used to recruit older adolescents (13–18) from socioeconomically deprived areas across UK. Area-level deprivation was used as a proxy for individual-level SEP to avoid stigmatising participants. In the first instance, schools across the East of England whose free school meal eligibility was in the highest quartile of the country, as defined by the Department of Education (> 18.8% of students), were contacted via email. Schools were provided with study information and asked if they would be willing to distribute this to their students. If no response was received, schools were sent a follow-up email and then contacted via phone.

Further strategies were developed due to the strain on schools in response to the Covid-19 pandemic and challenges in recruiting via this method. This included recruiting community groups (e.g. local youth groups or Scouts or Guides groups where young people participate in organised activities) across the East of England to facilitate participant recruitment. These groups were identified online and contacted via email if they were situated in one of the 20% most deprived areas in the UK, as defined by the Index of Multiple Deprivation (IMD) [ 27 ]. Community groups were also recruited via social media using a combination of direct messaging and study adverts. Recruited community groups were asked to share the study information with their group members.

Facebook and Instagram were used to run adverts targeted geographically towards adolescents living in deprived areas across the UK using the IMD (as described above). Finally, a snowball sampling technique was used where participants were offered a £15 Amazon voucher if they referred a friend and this friend took part.

Information was provided at the school, community group, and participant levels, outlining the purpose of the study and detailing what participation involved, including any potential risks and benefits, as well as the handling of the collected information. All participants recruited into the study provided informed consent, consent was also sought from the parent/guardian of participants < 16 years old. Participants provided the first 4 digits of their postcode to determine the IMD of their local area and the name of their school, this information was used as a proxy for Socioeconomic Position (SEP). As detailed above, only those living in the top 20% most deprived neighbourhood as determined by the IMD were eligible to participate. Using an information power approach, the final sample size was determined following an iterative process throughout data generation and analysis (see Supplementary File 2 for more information) [ 28 ] Table 1 .

Data generation

One-to-one semi structured interviews were conducted between July 2021 and March 2022. The majority of interviews were conducted using Zoom video teleconferencing software, due to restrictions imposed during the pandemic. The researcher leading data collection later became immersed in a local community group (when restrictions had eased), attending weekly group sessions (in person) between December 2021 and April 2022. In-person interviews were conducted one-to-one with young people recruited through the community group. Interviews took place in a private space during their usually group session.

A single interview guide was developed which directed participants through a timeline of their physical activity behaviour before and during the UK lockdowns and ended with a discussion of ideas around physical activity promotion moving forward. Social distancing measures were still in place at the start of interview period, the semi-structured nature of the topic guide allowed the researcher to tailor each interview to the participant’s individual context and the timing of the interview in relation to the easing of restriction measures. Interview questions and prompts were guided by the Socio Ecological Model (the topic guide is available in Supplementary File 3 ).

All interviews were conducted by the same researcher who had experience conducting semi-structured interviews with young people from deprived backgrounds. Time was taken at the start of each interview to provide an overview of the session, check participants were happy to proceed and ease participants into the interview. With consent, interviews were digitally audio recorded. Each young person attended one interview lasting ~ 30-minutes to be considerate of participants’ time, avoid interview fatigue and encourage engagement [ 29 ].

After each interview, a written summary was developed and sent to participants to check and provide feedback [ 30 ]. Everyone received a £15 Amazon voucher to thank them for their time. Young people had the opportunity to opt into a Photovoice element of the study for an additional £10 Amazon voucher, which involved taking photos of their physical activity environment. Participants were made aware that data and postage costs associated with this would be covered by funding from the study. None of the participants opted into this option.

All interviews were transcribed verbatim, anonymised and imported into Nvivo software (Version 12 Pro, QSR International, Victoria, Australia).

Transcripts were analysed guided by Braun and Clarke’s (2006) six phases of thematic analysis: (1) familiarisation with the data, (2) generating initial codes, (3) searching for themes, (4) reviewing themes (5) defining and naming themes; and (6) writing the report [ 31 ]. The researcher actively coded the transcripts, developing themes through layered interpretations of the data [ 32 ].

This began with the author immersing themselves in the dataset, listening to the audio recordings and reading the interview transcripts. Data were coded using both inductive (data-driven) and deductive (theory-driven) approaches [ 31 ]. Initially, we prioritised an inductive approach to generate codes from the data, focusing on the narratives provided by young people. This approach foregrounded young people’s perspectives and experiences. Subsequently, we applied deductive coding to structure these narratives by applying a socioecological lens. This allowed us to examine the interrelationships between young people and their social, physical and policy environment [ 23 ]. Initial codes were sorted into themes, capturing multiple observations in the data. This involved re-focusing the analysis at the broader level, considering how different codes may combine into overarching themes and sub-themes.

Candidate themes and sub-themes were developed and reviewed by rereading all the collected extracts for each theme. Once satisfied these adequately captured the code data, they were further refined developing clear names and definitions for each theme. After a fully worked-out set of themes had been developed, the research team worked to produce a compelling story about the data which strongly reflected the views and narrative of young people and the aims of this study. This is presented in the following sections.

Researcher characteristics and reflexivity

Adopting a subtle realist approach, the research team acknowledge the influence of their own subjective perception in their approach to the study. To maintain reflexivity the lead researcher and interviewer kept a journal documenting a self-critical account of the research process, including their immersion in a local community youth group. Throughout this time, they developed relationships with adolescents and staff members of the youth group, observing them in the context of their everyday lives. This undoubtedly influenced their approach to data analysis. Peer debriefing was used involving continual discussions about the research process and analysis, in addition to reflecting on researcher positionality and assumptions/subjectivities. More detail on researcher positionality and methods to enhance rigour and trustworthiness are outlined in Supplementary File 4 .

Public involvement and engagement

Methods for recruitment, interviewing and participant-facing materials were developed under the guidance of an existing youth advisory panel. Members of the group involved in this project were all between the ages of 13–18 years, to best represent the target age range of this project.

The following themes were generated during the data analysis: (1) Physical activity behaviour in everyday life (pre-pandemic), (2) The impact of Covid-19 on physical activity (during) and (3) Young people’s ideas about physical activity promotion (moving forward). A summary of each theme is outlined in Table  2 and Supplementary File 5 shows a thematic map for each theme.

Physical activity behaviour in everyday life (pre-pandemic)

What my physical activity looks like.

The young people participating in this study often labelled themselves as inactive. They described being most active during the week, through school travel, housework/caring roles, unstructured activity (e.g. skateboarding at the park) and Physical Education (PE). All participants used active travel to commute to school, often combining waking with other transport modes such as the school bus. Active travel was further used for leisure activities, one participant described, “It kind of depends , sometimes I walk , I just walk to the other villages , why not? I don’t really have another option” (Female , 16y) .

PE was described as a key contributor to weekly activity, with participants experiencing limited opportunities to be active beyond this setting, due to the barriers highlighted below (subtheme 2). When outlining their physical activity in a “usual” week, one young person described,

“Well , there is travel to and from school , probably like 5 minutes to walk to the bus stop and then the bus just takes me. But mainly 2 hours of PE in school and skating here (youth group) every Wednesday and Thursday” (Non-binary , 14y) .

Females at the upper end of the age spectrum conveyed the importance of dance as part of the school curriculum, whereas males more often referenced football, “Er , basically after school on weekdays just go out to play football with my mates…” (Male , 16y) . Outside of school, males more frequently reported participating in unstructured activities, whereas females were more likely to take on an active role at home through household chores or caring for siblings, thereby reducing their sedentary time.

“ TV at my dad’s but at my mum’s my younger sister , having to watch her… I had to keep going out there to watch my little sister because my little sister loves going on the trampoline” (Female , 13y) .

With the exception of a few participants living in close proximity to their school, most described being unable to participate in extra-curricular activities as they relied on the school bus to get home. Instead, participants engaged in informal physical activities such as playing football with their friends or going to the skatepark, “We don’t do training […] Some people just bring their football and we just play on the field and run with it.” (Male , 16y) .

Factors influencing my physical activity

A focus across participants was the barriers they faced to being active, including the previously mentioned transport barriers and having an after-school job. For most participants this meant they were unable to attend after school sports clubs. For example,

“…but that’s like for after school and I know that a lot of people like wouldn’t be able to do stuff like that because obviously they have to get home like early or something like that and they have to get the bus and some people have job they have to do” (Male , 16y) .

When discussing the lack of facilities in his local community, one participant described driving.

past a swimming pool on a school trip and how he wished facilities like this were available where he lived, “like why isn’t that in my area because I love swimming , I love swimming a lot so I think they should make it like more local” (Male 15y) . Building on this, the travel options available to many participants were described as being poor quality and dangerous which prevented them accessing physical activity opportunities. One participant explained, “Like you can go on a bike but it’s like really dangerous because of how fast cars go down those roads” (Male , 14y). Journey length using public transport was a further barrier, with participants reflecting on their limited access to clubs and facilities in the local community.

“I guess when , the only times I see like after school clubs are like places that are quite far actually … ” (Male 15y) .

The physical activity facilities that most participants could access were described as either of poor quality or in disrepair, with vandalism and safety concerns being major problems. Similar barriers were experienced in the school environment, with lack of and poor-quality equipment being a major focus in addition to limited activity choice, for example “There’s like football and that’s all I know of that’s doing physical activity” (Female , 18y) .

Identity and gendered experiences of physical activity

Being a ‘sporty’ person was identified as important for physical activity participation. Participants described being ‘sporty’ as someone who is athletic, enjoys sport and is good at it. Physical activity was associated with defining oneself or being seen as ‘sporty’ and being part of a ‘sporty’ friendship group. For example,

“ like there’s the group of people that do exercise like the football group and everybody else , you’re not really , don’t really do exercise or do anything like that. So , I think it’s so much that stereotype type , type of people of like , “Yeah , no , it’s not for me” (Female , 16y) .

Among female participants, feeling self-conscious about participating in physical activity was frequently mentioned. One participant described, “I wouldn’t personally want to like run around the streets or anything because I might see someone I know…” (Female , 16y) . Concerns about skill level were reported to compound this, for example, “Especially , yeah if you’re not good at the sport , like I think it’s another layer of like…” (Female , 18y). Male narratives primarily focused on skill level but framed this positively as they described how they enjoyed being skilful and making the most of opportunities to display this skill to others.

“You’ll be able to show off that you’re a good boxer or whatever , anything that you want to like , anything that you want to be a perk and yeah just make something of it you know” (Male , 16y).

A further point of discussion was the “gendered” nature of sport and how this limited the activity opportunities available to young people, especially at school. For example,

“Sports are all quite gendered , like boys play football , girls play netball in school. That’s one of the things that maybe just like I know quite a few boys who would love to play netball , but they can’t because they’re not allowed to” (Female , 13y) .

This was a prominent narrative among participants identifying as non-binary who further discussed the restrictive nature of the school PE kit, “The school PE kit we have it’s like you have to wear a skort and like a t-shirt , but the skorts are like really short compared to like boy’s shorts” (Non-binary , 13y).

The impact of covid-19 pandemic on physical activity

The impact on my physical activity behaviour.

Participants discussed the impact of the Covid-19 pandemic and resultant lockdowns on their physical activity. Lack of routine was frequently referred to, with activities such as traveling to school being replaced with sedentary activities, including watching TV or playing video games. Upon returning to school, all participants noticed a stark change in their activity levels due to the increase in structure.

“It was easier to be active because school was more structured instead of what it had been before (in the summer lockdown) so it…like I knew what my lessons were , then I can do my work , there’s like walking and stuff and had more of like a routine” (Female , 16y).

Importantly, young people did not feel this applied to online learning. During the winter lockdowns, online learning became more structured and the work volume increased. Participants felt this had a negative impact on their physical activity due to the increase in sedentary screen time and the long duration of online classes allowing little time for movement during the day. One participant explained,

“They (school) had even more work this time and we had lessons that lasted 2 hours and I barely had time to eat lunch. So I mean the first few weeks were the worst. I mean I once stayed ‘til 5 doing maths work because they put too much work” (Female , 13y).

Some found it difficult to engage in schoolwork at home and had to return to school for supervised lessons. This had a positive impact on their physical activity behaviour due to the reintroduction of active travel to school. One participant described,

“Some had , some people had to go to school because they weren’t doing the work because their parents wouldn’t make them. My mum made me do that because I was not doing work at home , she would come home from work and I was just sat watching TV. But then at least , at least I got to go out and do something and like I walked part of the way to school” (Male , 15y) .

The omission of PE and dance from the online curriculum was thought to contribute to a decrease in physical activity. For example, one participant described how she stopped dancing during the lockdowns as her school did not set work for her dance class, the participant used the term “non-curriculum based work” to refer to non-exam based school work as she was preparing to take her General Certificate of Secondary Education exams (GCSEs).

“Like they usually they set like non-curriculum based work for like all my other subjects , apart from dance , so like I didn’t do any dancing” (Female , 15y) .

The impact on my physical health

Two participants shared their struggle of weight gain during the lockdowns. This was a real challenge to their personal identity and had a negative impact on their physical and mental health and relationships.

“So I was like a floater , like a ghost or stuff , like I just followed like a group around the like the place so it was , I don’t know , I was just scared to speak to people because I feel felt like people were going to like make fun of me because the like me gaining weight was like noticeable as well. So I just didn’t want , I just didn’t want to speak because I was scared like people were going to start making fun of me and stuff” (Male , 16y) .

A common theme among young people was finding the return to “normal” difficult (during periods of eased lockdown restrictions). They reported negative impacts on their physical health including fitness loss, injury and weight gain. This included breathing difficulties when returning to PE and feeling more out of breath than expected. Sports injuries were also reported.

“I pulled my hamstring on my right leg , I think it was three times after lockdown. Because like I’d kick a football and I hadn’t been able to do that for like over 6 months” (Male , 16y) .

The impact on my mental health

Some participants described how they had struggled with poor mental health and low mood, communicating a nuanced understanding of the interrelationship between physical and mental health in relation to physical activity. For males, this was primarily experienced as anger, which was exacerbated by being confined at home. One participant discussed how the anger issues he experienced before the Covid-19 pandemic became worse during the lockdowns.

“Er…it probably affected my attitude because I was quite angry most of the time , I have anger problems so I just hit a window and my knuckles they had bent out of place” (Male , 16y).

Feeling uncertain about the future and a lack of control was also described as negatively impacting mental health. For some young people, the meaning of and motivation for physical activity changed during the pandemic. Physical activity was often used as a coping mechanism, especially walking. One participant described, “I had like intrusive thoughts sometimes that I needed to like , to try and get rid of , so walking really helped” (Female , 18y).

Subtheme 4: physical activity and the return to school during Covid-19 pandemic

When returning to school, the introduction of one-way systems, separating the school by year group zones and the removal of classroom rotations was reported to impact incidental physical activity like walking between lessons and recreational activity at break times.

“So we were constricted to our own zones , which meant that I wasn’t like playing ball and things like that as well. And because I’m top set in everything I was stuck in one classroom every day because they designated like a top set classroom , second set classroom and things like that , so I was just stuck in one classroom all day , just sitting there just waiting for my teacher to come” (Female , 13y).

Participants further described how social distancing measures impacted PE/dance lessons. This included being restricted to certain zones and types of activities, wearing masks and not having access to sports equipment or changing facilities. The quote below provides an example,

“At school it was like we had to go to school like on the days we had dance , we had to go in our like dance kit and then we’d do it like for an hour , and then we’d like carry on wearing it around the school , but like even then we had like boxes on the floor so we couldn’t do any like group work or like partners , and part of like the qualification is based on relationships in dance. And it was like we just couldn’t do that , and we had to wear masks the whole time , and the amount of people that had to sit down because they thought they were going to pass out , it was so bad” (Female , 15y).

Theme 3: young people’s ideas about how to promote physical activity

This final theme outlines participants’ recommendations for physical activity promotion moving forward. Interview questions specifically focused on each level of the Socio Ecological Model, reflected by the subthemes below.

Subtheme 1: ideas about physical activity promotion- individual

Narratives focusing on the individual centred around young people’s knowledge of the benefits of physical activity and identity in relation to being active. This was linked to earlier discussions around not identifying as “sporty” and how this impacted young people’s interest in being active.

“I just think , erm well , like if you are not sporty I just don’t think you will be interested in doing any activity , so I am not really sure what you can do” (Non-binary , 14y).

Many young people discussed promotion efforts to increase awareness of the physical and mental health benefits of physical activity, in addition to support for those with poor mental health. A further demonstration young people’s nuanced understanding of the relationship between physical and mental health.

“I think we need to help them (young people) with mental health and this would motivate them to do more physical activity. It isn’t really talked about you know , like the link between the two” (Female , 18y).

Recommendations included increasing awareness about the link between physical activity and mental health, for example, through school or social media channels targeting young people. Participants suggested providing online resources highlighting physical activity opportunities aimed at young people, such as adolescent only gym times and helplines to chat about how physical activity could impact their mental health.

Subtheme 2: ideas about physical activity promotion- interpersonal

When discussing promotion strategies, specific focus across participants was placed on peer relationships and the importance of friends as co-participants. Friends were described as facilitators in taking up opportunities and co-participation was suggested as a way to encourage more young people to be engaged in physical activity. Parents were recognised as important sources of support, but young people recognised that their parents had work or other competing life demands.

“I think it’d be a lot easier if like our friends did it because there’s a lot more of us , whereas I don’t think , like parents have like jobs and everything so they would be like time-restricted as well , but if we have like friends and stuff then we could all do it a lot easier together and I feel like that would better” (Female , 16y).

Participants outlined the importance of encouragement and guidance in helping young people gain autonomy over their physical activity. Emphasis was frequently placed on encouragement, but not enforcement, as enforcing physical activity could be counterproductive. This encouragement did not have to come from a particular source and any encouragement was seen as beneficial e.g. from teachers, friends, parents etc. One participant described,

“I think they just need that little extra push through like schools or like their communities , or like however , whatever form you’d want it to take , yeah , just to like introduce them to difference activities they could do and then they can decide what they do and don’t want to do” (Female , 16y) .

Subtheme 3: ideas about physical activity promotion- organisational

School-based promotion was a major focus and included expanding the PE curriculum and offering a greater range of activities. Linking back to their ideas around choice, young people often suggested increasing students’ autonomy during PE and proposed giving a selection of activities to choose from.

“I think it’s like the , the opportunity to have a choice in PE , you know , if they , you know , if they have like a whole array of sports to choose from to do and people could like just have the choice I think that’d be great , because you know , you’ve been told , yeah next week we’re doing hockey , remember your shin guards and your mouth guard and , you’d just be like , I don’t want to do this , when you’d rather prefer to like be doing like a dance lesson or rounders even , something like that” (Female , 18y).

Beyond this, participants agreed school-based promotion should focus on the school day in order to reach all students. Suggestions focused on break time provision, including providing sports equipment and opening up sports facilities for student use. Some suggested class-based activities with a competitive element or rewards for meeting physical activity targets.

“My sister in school has these things that are like challenges. And they gave , they give her rewards like and things , where she can do all the things with those points. And some of the challenges are physical activities , so maybe doing that is a good idea” (Female , 18y).

Contrasting earlier narratives about compulsory physical activity, participants suggested adding extra lessons to the PE curriculum. Some young people highlighted how, for older students, it was only possible to take part in PE if they chose it as an Advanced (A-) level subject. They thought that all A-level students should have the opportunity to participate in physical activity/PE, “There’s no PE at school , so I guess making it optional instead of just not having the PE” (Female , 18y).

Overall, discussions focused on having fun and how this was more likely to encourage young people to be active going forward. One participant described how when sport is fun, it doesn’t feel like exercise,

“I feel like , I don’t know , in school , you know in PE lessons , if they found like more fun sports. Because then you don’t even feel like you’re doing exercise , like in our school something they did was athletics and no-one really enjoyed it because it’s just running” (Female , 16y).

Subtheme 4: ideas about physical activity promotion- community

Discussions around community-based promotion focused on increasing the provision of opportunities and places to be active, for example:

“Um , I think maybe just having more opportunities and places that you can be active because I know in my , where I live particularly there is nowhere for young people to do activities” (Non-binary , 13y).

Relating back to transport barriers described in theme 1, many recommendations were made to develop better travel infrastructure with a focus on cycling, “Well , they could start putting paths in to like neighbouring towns , because I know quite a few people that would love to go visit neighbouring towns” (Female , 16y).

Subtheme 5: ideas about physical activity promotion- public policy

In general, participants were uncertain about the role of the government in physical activity promotion and discussed how young people might respond differently to promotion efforts.

“I think a lot of people would respond differently to like how government , how the government would roll out different , you know , different campaigns there could be , yeah. So yeah I probably couldn’t give a , a generalised opinion on how I think…” (Female , 18y).

Some suggested the government provide advice on physical activity, equipping young people with the knowledge and agency to be active. Relating to discussions at the community level, many proposed improving or developing physical activity facilities and adding equipment to parks. The cost of accessing activity clubs in the local community further discussed, with participants suggesting the government subsidise and/or increase the provision of free sports clubs.

“I was thinking of joining a badminton club , some of them can be like kind of expensive but if they’re made a bit cheaper for students they might want to go more” (Female , 16y).

This study aimed to understand, from the perspective of adolescents living in socioeconomically deprived communities, if and how the Covid-19 pandemic influenced their physical activity behaviour and their ideas about physical activity promotion moving forward. These perspectives are discussed below in the context of the broader literature base.

Physical activity in everyday life (pre-pandemic)

Young people described having limited access to structured physical activity opportunities, adding contextual understanding to inequities observed in self-reported physical activity, a method which favours the recall of organised sports participation [ 21 ]. Participants explained environmental factors in the community such as poor active travel infrastructure, access to sports clubs (locally and after-school clubs) and cost as barriers to such opportunities (pre-pandemic). This is consistent with existing qualitative and quantitative review-level evidence that highlights socioeconomic disparities in the built environment [ 13 , 14 , 33 ]. The community factors mentioned above provide direction for the equitable implementation of physical activity opportunities moving forward.

Participants discussed how the gendered nature of sport and school clothing can act as a barrier to participation in physical activity. This aligns with existent evidence focusing on children, which highlights that school uniforms can be a barrier to physical activity participation among girls, children from ethnic and religious minorities, gender-diverse students, and those from socioeconomically disadvantaged backgrounds [ 34 , 35 , 36 ]. Feeling self-conscious emerged as a common theme among females, as evidenced by qualitative research foregrounding pressure to perform, anxiety related to body image, and the reinforcement of gender stereotypes, especially among females from socioeconomically deprived contexts [ 13 ]. These findings underscore the intersection of gender and socioeconomic disparities in physical activity, with significant implications for inclusivity.

The impact of covid-19 on physical activity behaviour (during)

Young people’s accounts highlight the significant impact of the Covid-19 pandemic on their physical activity. This adds to self-reported survey data by showing the impact of the pandemic may have worsened trends in inactivity and exacerbated inequities in the UK [ 19 ]. these findings are consisted with claims that older adolescents and those from lower socioeconomic backgrounds were among the wort affected groups [ 7 ]. The absence of routine emerged as a major barrier to physical activity, echoing similar experiences of the pandemic reported by young people in low-income settings in the United States (US) [ 37 ]. These findings support the structured day hypothesis, which suggests obesogenic behaviours, including physical inactivity, are better regulated when young people have structured days such as school weekdays [ 38 ]. The removal of this structure has been linked with socioeconomic inequalities in young people’s health behaviour [ 39 ].

The adverse impact on young people’s mental health supports reports that the pandemic disproportionately affected the mental health of young people from lower socioeconomic conditions [ 40 ]. This shows the impact of the removal of school routine in a UK context, which had been associated with increased socioeconomic inequalities in mental health and wellbeing [ 39 ]. Young people’s discussions about the effects of the pandemic on their physical health mirror reports among the general adult population, where reduced physical activity and prolonged sedentary behaviour have been associated with loss of muscular and cardiorespiratory fitness and weight gain [ 41 ]. This is particularly concerning when considering that these effects may persist and worsen into adulthood.

Young people’s descriptions of the return to school reflect reports that schools in socioeconomically deprived areas were the worst affected, due to disparities in resources [ 42 ]. Our study highlights the implications of this for physical activity were especially stark, as schools were a primary source of physical activity among this group. Challenges faced by these schools appear similar beyond the UK context, with social distancing measures, access to a gymnasium, avoiding close contact and the use of uncleaned equipment reported as major barriers to PE [ 43 ]. This suggests the pandemic highlighted existent inequalities in school resources and the provision of physical activity opportunities.

Young people’s ideas about how to promote physical activity (moving forward)

Moving forward, Young people provided direction for physical activity promotion across the multiple levels of the Socio Ecological Model [ 23 ]. This included an increased focus on the benefits of physical activity, specifically the mental health benefits and the provision of mental health services. Friends were described as a primary source of social support and facilitators in taking up physical activity opportunities. This adds to the broader qualitative evidence, where adolescents living in the context of socioeconomic deprivation are reported to rely more heavily on social support from friends than family members [ 13 , 44 , 45 ].

There was a focus on school-based physical activity implemented during the school day, highlighting the importance of the school setting in health promotion among young people of a lower SEP [ 13 , 39 ]. The opportunity to practice numerous physical activities (during PE, school sports, break time and field trips) and autonomy over these was emphasised by participants and aligns with previous research highlighting the desire for increased autonomy among this age group [ 46 , 47 ], [ 48 ] Further emphasis was placed on promoting fun, rather than competition, a dominant narrative among qualitative research [ 46 ]. For example, placing less focus on “traditional” games sport such as football and netball and more focus on promoting physical activity as part of daily life. This contracts similar research with this population in the US, suggesting cultural differences might impact experiences of leveraging a competitive environment in physical activity promotion [ 37 ].

At the community level, young people’s recommendations support those of Rossi et al., (2021) who suggest moving forward policymakers and city planners increase access to safe and movement-friendly environments, especially in socioeconomically deprived areas [ 7 ]. This includes the provision of safe active travel infrastructure connecting places of importance to young people [ 8 , 48 , 49 ]. This was emphasised during discussions at the policy level, which focused on the structural environmental provision and access to low cost/free physical activity opportunities. In addition to the provision of support services for young people including physical activity and mental health helplines.

Strengths and limitations

Focusing on young people’s perspectives, this study adds in-depth, contextual understanding to existent evidence on physical activity behaviour during Covid-19. Exploring the experiences of young people progresses our understanding of inequalities in physical activity and the development of equitable physical activity promotion. Further strengths include the semi-structured interview format and the development of personal connections with participants recruited through a youth group setting, both of which allowed for the generation of nuanced insights on the interview topics and in the sharing of participants’ experiences.

The majority of the sample being from one region means the findings may not fully reflect experiences beyond this context. However, this is consistent with a qualitative approach [ 50 ]. Members of our Public Involvement and Engagement panel were generally of a higher SEP than those that we were aiming to recruit to this study which may have contributed to recruitment challenges and lack of engagement in the Photovoice element of the study. Interviews were conducted over a one-year period after the final UK lockdown, making it challenging for those in later interviews to recall their experiences. Moreover, it is important to acknowledge the possibility of social desirability bias in answering interview questions, especially during zoom interviews where it can be challenging to build a rapport.

Recommendations for research and practice

Young people’s focus on school-based interventions and the provision of facilities in their community highlight target areas for the development of equitable physical activity strategies and policy level change. These include investment in school-based interventions implemented during the school day and the provision of low-cost opportunities and active travel infrastructure in socioeconomically deprived areas. In order to make physical activity more inclusive, further efforts are required to move away from a gender focus in sport. This extends to school uniform policy, where modifying student uniforms and PE clothing may represent a simple intervention to enhance physical activity [ 36 ].

The importance of routine was emphasised, suggesting a need to prioritise the provision of structured physical activity opportunities [ 38 ]. This highlights the potential for future research exploring the development of the structured day hypothesis within the adolescent population, as current literature has focused predominantly on children. Its broader application to research investigating inequalities in adolescents’ experiences of routine in relation to health behaviour could have significant implications for policy and practice. For example, this could lead to target investment in the provision of free sports clubs during school holidays and in school-based interventions, such as offering after-school sports clubs during lunch breaks, especially at schools with a high proportion of students from socioeconomically disadvantaged backgrounds.

Moving forward, concerted efforts are required to reverse the decline in physical activity and address worsening mental health stemming from the pandemic. Young people made multiple recommendations, including the provision of online resources for discovering physical activity opportunities tailored to their age group, as well as mental health services which emphasise the benefits of physical activity. As highlighted by young people in the study, initiatives to promote physical activity have the potential to positively impact mental health, and vice versa. Targeted social media campaigns were proposed as one avenue for further exploration. Future research into how to effectively engage friends as a primary source of social support may enhance the success of these strategies.

This qualitative study of young people’s narratives suggests the Covid-19 pandemic may have contributed to trends in inactivity during adolescence and exacerbated inequalities. Moving forward, efforts to tackle inequalities and increase physical activity should include promoting a variety of school-based physical activity opportunities during the school day which focus on fun and the structural environmental regeneration of socioeconomically deprived areas to provide young people with safe and low-cost physical activity opportunities. As we build back from the Covid-19 pandemic, efforts to support the physical and mental health of young people living in the context of socioeconomic deprivation should be prioritised.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to the sensitive nature of the research, but are available from the corresponding author on reasonable request.

Abbreviations

• United Kingdom

Standards for Reporting in Qualitative Research

Index of Multiple Deprivation

Socioeconomic Position

Physical Education

General Certificate of Secondary Education

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Acknowledgements

We would like to acknowledge all the volunteers who generously gave their time, those who participated in this research and who contributed to the patient and public involvement and engagement. We are extremely grateful to the young people who shared their ideas and experiences and parents, teachers and youth leaders who took an interest in the study and helped to get young people involved.

Olivia Alliott is funded by the National Institute for Health and Care Research (NIHR) School for Public Health Research (SPHR), Grant Reference Number PD-SPH-2015. The NIHR School for Public Health Research is a partnership between the Universities of Bristol, Cambridge and Sheffield; Imperial; and University College London; The London School for Hygiene and Tropical Medicine (LSHTM); LiLaC – a collaboration between the Universities of Liverpool and Lancaster; and Fuse – The Centre for Translational Research in Public Health a collaboration between Newcastle, Durham, Northumbria, Sunderland and Teesside Universities. Hannah Fairbrother is funded through the University of Sheffield. The work of Esther van Sluijs is supported by the Medical Research Council (grant number MC_UU_00006/5).

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Olivia Alliott & Esther van Sluijs

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OA led on all aspects of this study with support from EvS and HF. Conceptualisation: OA, EvS, HF. Data curation: OA, Formal analysis: OA, Methodology: OA, EvS, HF, Project administration: OA, Writing—original draft: OA, Writing—review and editing: OA, EvS, HF. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Esther van Sluijs .

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Ethical approval for the study was granted by the University of Cambridge School of Humanities and Social Sciences ethics committee (21.273). All participants recruited into the study provided informed consent, consent was also sought from the parent/guardian of participants < 16 years old.

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Primary qualitative data collection and analysis.

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Alliott, O., Fairbrother, H. & van Sluijs, E. Adolescents’ physical activity during and beyond the Covid-19 pandemic: a qualitative study exploring the experiences of young people living in the context of socioeconomic deprivation. BMC Public Health 24 , 2450 (2024). https://doi.org/10.1186/s12889-024-19777-z

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Susanne Mehrer Reappointed as Dean of Libraries

Mehrer’s third term as head of Dartmouth Libraries officially begins in July.

'A Solemn and Celebratory Occasion'

Dean of Libraries Susanne Mehrer has been appointed to a third term, beginning July 1, 2025, Provost David Kotz ’86 has announced.

Mehrer, who was previously deputy university librarian at the University of Cambridge, joined the Dartmouth community in December 2016 and was reappointed to a second term in 2020. 

“A strong library system is the heart and soul of any academic institution, and Dartmouth is fortunate to have in Susanne Mehrer a passionate, forward-looking, and collaborative leader,” says Kotz. “Sue understands the immeasurable value the Dartmouth Libraries add to our educational and research mission, and she is fully committed to making sure the library more than meets the fast-changing needs of today’s students and scholars.”

“Dartmouth is a special place,” Mehrer says. “What attracted me originally was that it’s a close-knit academic community that operates at the highest research level. There is a personal aspect to working here that I appreciate. I love that I can pick up the phone and say to a dean, a faculty member, whomever, ‘Can we have a conversation?’ And I don’t do anything alone. I have a fantastic team and incredible colleagues, and what we do, we do as a team and an organization.”

We see Dartmouth Libraries as firmly part of the ecosystem of creating new knowledge.

Most recently, Mehrer led the Dartmouth Libraries’ strategic planning process , which engaged stakeholders across the institution, including faculty, students, staff, senior leadership, and other campus partners, as well the Board of Trustees.

“We’ve had this amazing input from our community, and I want to make sure that we continue to translate that into action,” Mehrer says.

The strategy is built on four pillars: empowering students’ individual potential, accelerating advanced research, elevating scholarship with cutting-edge research tools and methods, and helping to amplify the impact of Dartmouth scholarship.

“We see Dartmouth Libraries as firmly part of the ecosystem of creating new knowledge. We touch on every part of the research life cycle and the learning cycle that leads our students into research,” Mehrer says. “We are directly supporting Dartmouth’s academic mission of creating new knowledge and President Beilock’s priority of advancing innovation and impact.”

Among other accomplishments, Mehrer created opportunities for Dartmouth Libraries to meet the changing demands of research in the 21st century, growing its capacity in emerging areas, including open scholarship, digital collections, research data, and artificial intelligence. 

“Research is changing as we speak, and we are playing our part in making resources and expertise available, guiding our students, partnering with our faculty, and making sure that the great research that is done here can be accessed by as many people as possible,” she says.

Mehrer has championed the Historic Accountability Student Research Program , which provides intensive experiences for undergraduates to work with primary sources and archives to surface important stories from Dartmouth’s past. “It’s an amazing opportunity for students to get individualized research experience that they might not get at other institutions,” Mehrer says.

She also helped oversee the construction of a new, 20,000-square-foot Library Collections and Services Facility to increase the library’s capacity to care for its physical collections. The facility opened this summer. “It is a purpose-built, industry-standard facility where we can now steward and curate the collections going forward,” she says. “It speaks to Dartmouth’s recognition that physical collections will always be part of the liberal arts education.”

During the COVID-19 pandemic, Mehrer and her team prioritized maintaining access to the library’s resources. 

“We never shut down,” she says.“We had to close our doors for the two months that the state had everybody close their doors, but even during those times, a small team of us were here, and we provided a click-and-collect service so that faculty and researchers still had access to the physical materials that we hold. We never stopped making our collections accessible. The staff pulled out all the stops to make sure that we can be as effective for our community as possible. And they were incredibly creative. One staff member sent out packages to students working with book arts. Special Collections rigged up a way to digitally teach a class with archival materials. It was hard, but we learned a lot—and there were lessons in there that we don’t want to lose.”

Closest to her heart, Mehrer says, was the opportunity in 2022 for the library to participate in the repatriation of the papers of Samson Occom to the Mohegan Tribe . Occom, a Mohegan scholar and minister who is considered one of Dartmouth’s founders, traveled to Great Britain in 1766 at the request of the Rev. Eleazar Wheelock, his former teacher, to raise money for what would ultimately become Dartmouth College. “Being part of repairing Dartmouth’s relationship with the Mohegan Tribe was a deeply meaningful experience, and was personally very moving.”

Mehrer completed her undergraduate degree at the University of British Columbia, majoring in English and German, and went on to earn a master’s in English and an executive MBA at Queen’s University Belfast, a postgraduate diploma in library and information studies from the University of Wales Aberystwyth, and an MA from Cambridge.

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A lot of people would have hoped that we would see more improvement over 125 years, and we haven’t.

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