essay support systems

Elevate your teaching at Paper’s IDEA Conference in Las Vegas, June 12-14

Build confidence for learning—and life

Unlock opportunities to empower every student in their learning journey, from kindergarten to senior year and beyond.

Meet your Educational Support System

Personalized academic support

Drive learning gains with 24/7 tutoring that allows every student to ask questions, prepare for exams, and submit their best work for every class.

Interactive, independent practice

Build foundational skills with interactive, grade-level appropriate tools that provide fluency, vocabulary, reading comprehension, and math practice.

Future exploration and guidance

Help students chart the post-secondary pathway of their choice with tools for academic planning, career test drives, and work-based learning.

Serving 3 million learners nationwide

See our impact  →

Solving hard problems for K-12 stakeholders

Administrator.

We deliver equitable, cost-effective, and impactful academic support that drives learning acceleration, student engagement, and future readiness.

See the administrator experience →

We help teachers unlock more time and opportunities to empower students, with afterschool and in-class support as well as insights from tutoring sessions that can help tailor instruction.

See the teacher experience →

We provide free, personalized, on-demand resources to help every student succeed in school, while building skills that will make them confident lifelong learners.

See the student experience →

Awards and recognition

Thinking of bringing paper to your schools, resources from our library.

In Texas, Mesquite ISD fosters student confidence with 24/7 academic support

Learn how Mesquite school district in Texas fostered student confidence and self-advocacy with 24/7 individualized academic support.

Teachers talk: The best strategies to drive student motivation

Teachers told us how they fuel student motivation through choice, relationships, real-world relevancy, and more. Take a ...

Teachers share the tangible—and intangible—rewards of the job

Educators shared with us the many forms of appreciation and recognition that keep them inspired. Find out what fuels the...

Career and college readiness insights from today’s education leaders

We spoke with K-12 leaders from across the country to hear what’s on their minds. Find out how top superintendents appro...

Developing Support

Types of support.

There are many types of support, depending on the purpose of your essay. Supporting sentences usually offer some of the following:

• example :  The refusal of the baby boom generation to retire is contributing to the current lack of available jobs.
• example :  Many families now rely on older relatives to support them financially.
• example :  Nearly 10 percent of adults are currently unemployed in the United States.(citation would be included here)
• example :  “We will not allow this situation to continue,” stated Senator Johns (citation would be included here).
• example :  Last year, Bill was asked to retire at the age of fifty-five.
• example: I have known other workers at my current workplace who have been less directly moved out of their jobs, through changes in job duties and other tactics that are directed at making them want to retire, or at least leave their current position.
• example: In an interview accessed online, Bill Gates expressed his optimism that privacy and government access of information will be balanced – that it’s not an either/or situation. (https://www.youtube.com/watch?v=WxavGoTUPrc)

The types of support you develop and include in an essay will depend on what you are writing and why you are writing. For example, if you’re attempting to persuade your audience to take a particular position you might rely on facts, statistics, and concrete examples, rather than personal opinions. If you are writing an essay based on your observations, you might rely on those observations along with examples and reasons. If you are writing a research essay, you might include more quotations, reasons, and facts. Realize, though, that all types of support are usable in all types of essays, and that you often will have many or all of these types of support within one unit of support. The purpose of the essay simply lets you know the type of support you may want to emphasize.

Here’s an example of one supporting paragraph that uses many types of support:

(Topic sentence) There are numerous advantages to owning a hybrid car.  (Supporting sentence 1: statistic) First, they get 20 percent to 35 percent more miles to the gallon than a fuel-efficient gas-powered vehicle.  (Supporting sentence 2: fact) Second, they produce very few emissions during low speed city driving.  (Supporting sentence 3: reason) Because they do not require gas, hybrid cars reduce dependency on fossil fuels, which helps lower prices at the pump.  (Supporting sentence 4: example) Alex bought a hybrid car two years ago and has been extremely impressed with its performance.  (Supporting sentence 5: quotation) “It’s the cheapest car I’ve ever had,” she said. “The running costs are far lower than previous gas powered vehicles I’ve owned.”  (Concluding sentence) Given the low running costs and environmental benefits of owning a hybrid car, it is likely that many more people will follow Alex’s example in the near future.

Although it’s really useful to understand that there are different types of support, realize that as writers develop support, they don’t necessarily think in terms of “I need a fact here” or “I need an observation there.” It’s often best to simply write your ideas down in the first stage of developing your support. Conscious consideration of different types of support occurs as you continue to work with and review your support, in terms of your writing purpose and audience.

For example, a research essay that offers only statistics and facts may become boring to your audience if not interspersed with your own interpretations and observations and reasons. A personal observation essay may not get your thesis point across to your audience well if it doesn’t include multiple, specific examples. Draft your support first, and then go back to the draft to develop it further, the second time with your purpose, audience, and types of support more consciously in mind.

The following video discusses the elements of a strong supporting paragraph and illustrates types of evidence.

• Types of Support. Revision and adpatation of the page 6.2 Effective Means for Writing a Paragraph at https://saylordotorg.github.io/text_writing-for-success/s10-02-effective-means-for-writing-a-.html. Authored by : Susan Oaks. Project : College Writing. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• 6.2 Effective Means for Writing a Paragraph. Provided by : Saylor Academy. Located at : https://saylordotorg.github.io/text_writing-for-success/s10-02-effective-means-for-writing-a-.html . Project : Writing for Success. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• image of laptop with sticky notes covering the screen. Authored by : geralt. Provided by : Pixabay. Located at : https://pixabay.com/en/bulletin-board-laptop-computer-3233653/ . License : CC0: No Rights Reserved
• video How to write a supporting paragraph. Authored by : mistersato411. Located at : https://www.youtube.com/watch?v=Ylsprqayomg . License : Other . License Terms : YouTube video

• (800) 596-0724
• Request Info

Herzing Staff

Navigating the Journey: 6 Tips to Earn Your MBA While Working Full-Time

Nursing Intensives: Connecting Virtually, Learning Actively

Leading the Way to Better Care: The Impact of Healthcare Administration

Intersectionality in the Classroom: Understanding and Embracing Our Differences

• Scholarships
• Nursing Programs
• Campus Locations

Contact us to request more information

Opt-In to Receive SMS Messages

Get the latest news you need to know, from study hacks to interview tips to career advancement. Have it delivered right to your inbox biweekly.

Automated Essay Scoring Systems

• Reference work entry
• Open Access
• First Online: 01 January 2023
• pp 1057–1071
• Cite this reference work entry

You have full access to this open access reference work entry

• Dirk Ifenthaler 3 , 4  

19k Accesses

Essays are scholarly compositions with a specific focus on a phenomenon in question. They provide learners the opportunity to demonstrate in-depth understanding of a subject matter; however, evaluating, grading, and providing feedback on written essays are time consuming and labor intensive. Advances in automated assessment systems may facilitate the feasibility, objectivity, reliability, and validity of the evaluation of written prose as well as providing instant feedback during learning processes. Measurements of written text include observable components such as content, style, organization, and mechanics. As a result, automated essay scoring systems generate a single score or detailed evaluation of predefined assessment features. This chapter describes the evolution and features of automated scoring systems, discusses their limitations, and concludes with future directions for research and practice.

You have full access to this open access chapter,  Download reference work entry PDF

Similar content being viewed by others

An automated essay scoring systems: a systematic literature review

Automated Essay Feedback Generation in the Learning of Writing: A Review of the Field

• Automated essay scoring
• Essay grading system
• Writing assessment
• Natural language processing
• Educational measurement
• Technology-enhanced assessment
• Automated writing evaluation

Introduction

Educational assessment is a systematic method of gathering information or artifacts about a learner and learning processes to draw inferences of the persons’ dispositions (E. Baker, Chung, & Cai, 2016 ). Various forms of assessments exist, including single- and multiple-choice, selection/association, hot spot, knowledge mapping, or visual identification. However, using natural language (e.g., written prose or essays) is regarded as the most useful and valid technique for assessing higher-order learning processes and learning outcomes (Flower & Hayes, 1981 ). Essays are scholarly analytical or interpretative compositions with a specific focus on a phenomenon in question. Valenti, Neri, and Cucchiarelli ( 2003 ) as well as Zupanc and Bosnic ( 2015 ) note that written essays provide learners the opportunity to demonstrate higher order thinking skills and in-depth understanding of a subject matter. However, evaluating, grading, and providing feedback on written essays are time consuming, labor intensive, and possibly biased by an unfair human rater.

For more than 50 years, the concept of developing and implementing computer-based systems, which may support automated assessment and feedback of written prose, has been discussed (Page, 1966 ). Technology-enhanced assessment systems enriched standard or paper-based assessment approaches, some of which hold much promise for supporting learning processes and learning outcomes (Webb, Gibson, & Forkosh-Baruch, 2013 ; Webb & Ifenthaler, 2018 ). While much effort in institutional and national systems is focused on harnessing the power of technology-enhanced assessment approaches in order to reduce costs and increase efficiency (Bennett, 2015 ), a range of different technology-enhanced assessment scenarios have been the focus of educational research and development, however, often at small scale (Stödberg, 2012 ). For example, technology-enhanced assessments may involve a pedagogical agent for providing feedback during a learning process (Johnson & Lester, 2016 ). Other scenarios of technology-enhanced assessments include analyses of a learners’ decisions and interactions during game-based learning (Bellotti, Kapralos, Lee, Moreno-Ger, & Berta, 2013 ; Kim & Ifenthaler, 2019 ), scaffolding for dynamic task selection including related feedback (Corbalan, Kester, & van Merriënboer, 2009 ), remote asynchronous expert feedback on collaborative problem-solving tasks (Rissanen et al., 2008 ), or semantic rich and personalized feedback as well as adaptive prompts for reflection through data-driven assessments (Ifenthaler & Greiff, 2021 ; Schumacher & Ifenthaler, 2021 ).

It is expected that such technology-enhanced assessment systems meet a number of specific requirements, such as (a) adaptability to different subject domains, (b) flexibility for experimental as well as learning and teaching settings, (c) management of huge amounts of data, (d) rapid analysis of complex and unstructured data, (e) immediate feedback for learners and educators, as well as (f) generation of automated reports of results for educational decision-making.

Given the on-going developments in computer technology, data analytics, and artificial intelligence, there are advances in automated assessment systems, which may facilitate the feasibility, objectivity, reliability, and validity of the assessment of written prose as well as providing instant feedback during learning processes (Whitelock & Bektik, 2018 ). Accordingly, automated essay grading (AEG) systems, or automated essay scoring (AES systems, are defined as a computer-based process of applying standardized measurements on open-ended or constructed-response text-based test items. Measurements of written text include observable components such as content, style, organization, mechanics, and so forth (Shermis, Burstein, Higgins, & Zechner, 2010 ). As a result, the AES system generates a single score or detailed evaluation of predefined assessment features (Ifenthaler, 2016 ).

This chapter describes the evolution and features of automated scoring systems, discusses their limitations, and concludes with future directions for research and practice.

Synopsis of Automated Scoring Systems

The first widely known automated scoring system, Project Essay Grader (PEG), was conceptualized by Ellis Battan Page in late 1960s (Page, 1966 , 1968 ). PEG relies on proxy measures, such as average word length, essay length, number of certain punctuation marks, and so forth, to determine the quality of an open-ended response item. Despite the promising findings from research on PEG, acceptance and use of the system remained limited (Ajay, Tillett, & Page, 1973 ; Page, 1968 ). The advent of the Internet in the 1990s and related advances in hard- and software introduced a further interest in designing and implementing AES systems. The developers primarily aimed to address concerns with time, cost, reliability, and generalizability regarding the assessment of writing. AES systems have been used as a co-rater in large-scale standardized writing assessments since the late 1990s (e.g., e-rater by Educational Testing Service). While initial systems focused on English language, a wide variety of languages have been included in further developments, such as Arabic (Azmi, Al-Jouie, & Hussain, 2019 ), Bahasa Malay (Vantage Learning, 2002 ), Hebrew (Vantage Learning, 2001 ), German (Pirnay-Dummer & Ifenthaler, 2011 ), or Japanese (Kawate-Mierzejewska, 2003 ). More recent developments of AES systems utilize advanced machine learning approaches and elaborated natural language processing algorithms (Glavas, Ganesh, & Somasundaran, 2021 ).

For almost 60 years, different terms related to automated assessment of written prose have been used mostly interchangeably. Most frequently used terms are automated essay scoring (AES) and automated essay grading (AEG); however, more recent research used the term automated writing evaluation (AWE) and automated essay evaluation (AEE) (Zupanc & Bosnic, 2015 ). While the above-mentioned system focuses on written prose including several hundred words, another field developed focusing on short answers referred to as automatic short answer grading (ASAG) (Burrows, Gurevych, & Stein, 2015 ).

Functions of Automated Scoring Systems

AES systems mimic human evaluation of written prose by using various methods of scoring, that is, statistics, machine learning, and natural language processing (NLP) techniques. Implemented features of AES systems vary widely, yet they are mostly trained with large sets of expert-rated sample open-ended assessment items to internalize features that are relevant to human scoring. AES systems compare the features in training sets to those in new test items to find similarities between high/low scoring training and high/low scoring new ones and then apply scoring information gained from training sets to new item responses (Ifenthaler, 2016 ).

The underlying methodology of AES systems varies; however, recent research mainly focuses on natural language processing approaches (Glavas et al., 2021 ). AES systems focusing on content use Latent Semantic Analysis (LSA) which assumes that terms or words with similar meaning occur in similar parts of written text (Wild, 2016 ). Other content-related approaches include Pattern Matching Techniques (PMT). The idea of depicting semantic structures, which include concepts and relations between the concepts, has its source in two fields: semantics (especially propositional logic) and linguistics. Semantic oriented approaches include Ontologies and Semantic Networks (Pirnay-Dummer, Ifenthaler, & Seel, 2012 ). A semantic network represents information in terms of a collection of objects (nodes) and binary associations (directed labeled edges), the former standing for individuals (or concepts of some sort), and the latter standing for binary relations over these. Accordingly, a representation of knowledge in a written text by means of a semantic network corresponds with a graphical representation where each node denotes an object or concept, and each labeled being one of the relations used in the knowledge representation. Despite the differences between semantic networks, three types of edges are usually contained in all network representation schemas (Pirnay-Dummer et al., 2012 ): (a) Generalization: connects a concept with a more general one. The generalization relation between concepts is a partial order and organizes concepts into a hierarchy. (b) Individualization: connects an individual (token) with its generic type. (c) Aggregation: connects an object with its attributes (parts, functions) (e.g., wings – part of – bird). Another method of organizing semantic networks is partitioning which involves grouping objects and elements or relations into partitions that are organized hierarchically, so that if partition A is below partition B, everything visible or present in B is also visible in A unless otherwise specified (Hartley & Barnden, 1997 ).

From an information systems perspective, understood as a set of interrelated components that accumulate, process, store, and distribute information to support decision making, several preconditions and processes are required for a functioning AES system (Burrows et al., 2015 ; Pirnay-Dummer & Ifenthaler, 2010 ):

Assessment scenario: The assessment task with a specific focus on written prose needs to be designed and implemented. Written text is being collected from learners and from experts (being used as a reference for later evaluation).

Preparation: The written text may contain characters which could disturb the evaluation process. Thus, a specific character set is expected. All other characters may be deleted. Tags may be also deleted, as are other expected metadata within each text.

Tokenizing: The prepared text gets split into sentences and tokens. Tokens are words, punctuation marks, quotation marks, and so on. Tokenizing is somewhat language dependent, which means that different tokenizing methods are required for different languages.

Tagging: There are different approaches and heuristics for tagging sentences and tokens. A combination of rule-based and corpus-based tagging seems most feasible when the subject domain of the content is unknown to the AES system. Tagging and the rules for it is a quite complex field of linguistic methods (Brill, 1995 ).

Stemming: Specific assessment attributes may require that flexions of a word will be treated as one (e.g., the singular and plural forms “door” and “doors”). Stemming reduces all words to their word stems.

Analytics: Using further natural language processing (NLP) approaches, the prepared text is analyzed regarding predefined assessment attributes (see below), resulting in models and statistics.

Prediction: Further algorithms produce scores or other output variables based on the analytics results.

Veracity: Based on available historical data or reference data, the analytics scores are compared in order to build trust and validity in the AES result.

Common assessment attributes of AES have been identified by Zupanc and Bosnic ( 2017 ) including linguistic (lexical, grammar, mechanics), style, and content attributes. Among 28 lexical attributes, frequencies of characters, words, sentences are commonly used. More advanced lexical attributes include average sentence length, use of stopwords, variation in sentence length, or the variation of specific words. Other lexical attributes focus on readability or lexical diversity utilizing specific measures such as Gunning Fox index, Nominal ratio, Type-token-ratio (DuBay, 2007 ). Another 37 grammar attributes are frequently implemented, such as number of grammar errors, complexity of sentence tree structure, use of prepositions and forms of adjectives, adverbs, nouns, verbs. A few attributes focus on mechanics, for example, the number of spellchecking errors, the number of capitalization errors, or punctuation errors. Attributes that focus on content include similarities with source or reference texts or content-related patterns (Attali, 2011 ). Specific semantic attributes have been described as concept matching and proposition matching (Ifenthaler, 2014 ). Both attributes are based on similarity measures (Tversky, 1977 ). Concept matching compares the sets of concepts (single words) within a written text to determine the use of terms. This measure is especially important for different assessments which operate in the same domain. Propositional matching compares only fully identical propositions between two knowledge representations. It is a good measure for quantifying complex semantic relations in a specific subject domain. Balanced semantic matching measure uses both concepts and propositions to match the semantic potential between the knowledge representations. Such content or semantic oriented attributes focus on the correctness of content and its meaning (Ifenthaler, 2014 ).

Overview of Automated Scoring Systems

Instructional applications of automated scoring systems are developed to facilitate the process of scoring and feedback in writing classrooms. These AES systems mimic human scoring by using various attributes; however, implemented attributes vary widely.

The market of commercial and open-source AES systems has seen a steady growth since the introduction of PEG. The majority of available AES systems extract a set of attributes from written prose and analyze it using some algorithm to generate a final output. Several overviews document the distinct features of AES systems (Dikli, 2011 ; Ifenthaler, 2016 ; Ifenthaler & Dikli, 2015 ; Zupanc & Bosnic, 2017 ). Burrows et al. ( 2015 ) identified five eras throughout the almost 60 years of research in AES: (1) concept mapping, (2) information extraction, (3) corpus-based methods, (4) machine learning, and (5) evaluation.

Zupanc and Bosnic ( 2017 ) note that four commercial AES systems have been predominant in application: PEG, e-rater, IEA, and IntelliMetric. Open access or open code systems have been available for research purposes (e.g., AKOVIA); however, they are yet to be made available to the general public. Table 1 provides an overview of current AES systems, including a short description of the applied assessment methodology, output features, information about test quality, and specific requirements. The overview is far from being complete; however, it includes major systems which have been reported in previous summaries and systematic literature reviews on AES systems (Burrows et al., 2015 ; Dikli, 2011 ; Ifenthaler, 2016 ; Ifenthaler & Dikli, 2015 ; Ramesh & Sanampudi, 2021 ; Zupanc & Bosnic, 2017 ). Several AES systems also have instructional versions for classroom use. In addition to their instant scoring capacity on a holistic scale, the instructional AES systems are capable of generating diagnostic feedback and scoring on an analytic scale as well. The majority of AES systems use focus on style or content-quality and use NLP algorithms in combination with variations of regression models. Depending on the methodology, AES system requires training samples for building a reference for future comparisons. However, the test quality, precision, or accuracy of several AES systems is publicly not available or has not been reported in rigorous empirical research (Wilson & Rodrigues, 2020 ).

Open Questions and Directions for Research

There are several concerns regarding the precision of AES systems and the lack of semantic interpretation capabilities of underlying algorithms. Reliability and validity of AES systems have been extensively investigated (Landauer, Laham, & Foltz, 2003 ; Shermis et al., 2010 ). The correlations and agreement rates between AES systems and expert human raters have been found to be fairly high; however, the agreement rate is not at the desired level yet (Gierl, Latifi, Lai, Boulais, & Champlain, 2014 ). It should be noted that many of these studies highlight the results of adjacent agreement between humans and AES systems rather than those of exact agreement (Ifenthaler & Dikli, 2015 ). Exact agreement is harder to achieve as it requires two or more raters to assign the same exact score on an essay while adjacent agreement requires two or more raters to assign a score within one scale point of each other. It should also be noted that correlation studies are mostly conducted at high-stakes assessment settings rather than classroom settings; therefore, AES versus human inter-rater reliability rates may not be the same in specific assessment settings. The rate is expected to be lower in the latter since the content of an essay is likely to be more important in low-stakes assessment contexts.

The validity of AES systems has been critically reflected since the introduction of the initial applications (Page, 1966 ). A common approach for testing validity is the comparison of scores from AES systems with those of human experts (Attali & Burstein, 2006 ). Accordingly, questions arise about the role of AES systems promoting purposeful writing or authentic open-ended assessment responses, because the underlying algorithms view writing as a formulaic act and allows writers to concentrate more on the formal aspects of language such as origin, vocabulary, grammar, and text length with little or no attention to the meaning of the text (Ifenthaler, 2016 ). Validation of AES systems may include the correct use of specific assessment attributes, the openness of algorithms, and underlying aggregation and analytics techniques, as well as a combination of human and automated approaches before communicating results to learners (Attali, 2013 ). Closely related to the issue of validity is the concern regarding reliability of AES systems. In this context, reliability assumes that AES systems produce repeatedly consistent scores within and across different assessment conditions (Zupanc & Bosnic, 2015 ). Another concern is the bias of underlying algorithms, that is, algorithms have their source in a human programmer which may introduce additional error structures or even features of discrimination (e.g., cultural bias based on selective text corpora). Criticism has been put toward commercial marketing of AES systems for speakers of English as a second or foreign language (ESL/EFL) when the underlying methodology has been developed based on English language with native-English speakers in mind. In an effort to assist ESL/EFL speakers in writing classrooms, many developers have incorporated a multilingual feedback function in the instructional versions of AES systems. Receiving feedback in the first language has proven benefits, yet it may not be sufficient for ESL/EFL speakers to improve their writing in English. It would be more beneficial for non-native speakers of English if developers take common ESL/EFL errors into consideration when they build algorithms in AES systems. Another area of concern is that writers can trick AES systems. For instance, if the written text produced is long and includes certain type of vocabulary that the AES system is familiar with, an essay can receive a higher score from AES regardless of the quality of its content. Therefore, developers have been trying to prevent cheating by users through incorporating additional validity algorithms (e.g., flagging written text with unusual elements for human scoring) (Ifenthaler & Dikli, 2015 ). The validity and reliability concerns result in speculations regarding the credibility of AES systems considering that the majority of the research on AES is conducted or sponsored by the developing companies. Hence, there is a need for more research that addresses the validity and reliability issues raised above and preferably those conducted by independent researchers (Kumar & Boulanger, 2020 ).

Despite the above-mentioned concerns and limitation, educational organizations choose to incorporate instructional applications of AES systems in classrooms, mainly to increase student motivation toward writing and reducing workload of involved teachers. They assume that if AES systems assist students with the grammatical errors in their writings, teachers will have more time to focus on content related issues. Still, research on students’ perception on AES systems and the effect on motivation as well as on learning processes and learning outcomes is scarce (Stephen, Gierl, & King, 2021 ). In contrast, educational organizations are hesitant in implementing AES systems mainly because of validity issues related to domain knowledge-based evaluation. As Ramesh and Sanampudi ( 2021 ) exemplify, the domain-specific meaning of “cell” may be different in biology or physics. Other concerns that may lower the willingness to adopt of AES systems in educational organizations include fairness, consistency, transparency, privacy, security, and ethical issues (Ramineni & Williamson, 2013 ; Shermis, 2010 ).

AES systems can make the result of an assessment available instantly and may produce immediate feedback whenever the learner needs it. Such instant feedback provides autonomy to the learner during the learning process, that is, learners are not depended on possibly delayed feedback from teachers. Several attributes implemented in AES systems can produce an automated score, for instance, correctness of syntactic aspects. Still, the automated and informative feedback regarding content and semantics is limited. Alternative feedback mechanisms have been suggested, for example, Automated Knowledge Visualization and Assessment (AKOVIA) provides automated graphical feedback models, generated on the fly, which have been successfully tested for preflection and reflection in problem-based writing tasks (Lehmann, Haehnlein, & Ifenthaler, 2014 ). Other studies using AKOVIA feedback models highlight the benefits of availability of informative feedback whenever the learner needs it and its identical impact on problem solving when compared with feedback models created by domain experts (Ifenthaler, 2014 ).

Questions for future research focusing on AES systems may focus on (a) construct validity (i.e., comparing AES systems with other systems or human rater results), (b) interindividual and intraindividual consistency and robustness of AES scores obtained (e.g., in comparison with different assessment tasks), (c) correlative nature of AES scores with other pedagogical or psychological measures (e.g., interest, intelligence, prior knowledge), (d) fairness and transparency of AES systems and related scores, as well as (e) ethical concerns related to AES systems, (f) (Elliot & Williamson, 2013 ). From a technological perspective, (f) the feasibility of the automated scoring system (including training of AES using prescored, expert/reference, comparison) is still a key issue with regard to the quality of assessment results. Other requirements include the (g) instant availability, accuracy, and confidence of the automated assessment. From a pedagogical perspective, (h) the form of the open-ended or constructed-response test needs to be considered. The (i) assessment capabilities of the AES system, such as the assessment of different languages, content-oriented assessment, coherence assessment (e.g., writing style, syntax, spelling), domain-specific features assessment, and plagiarism detection, are critical for a large-scale implementation. Further, (j) the form of feedback generated by the automated scoring system might include simple scoring but also rich semantic and graphical feedback. Finally, (k) the integration of an AES system into existing applications, such as learning management systems, needs to be further investigated by developers, researchers, and practitioners.

Implications for Open, Distance, and Digital Education

The evolution of Massive Open Online Courses (MOOCs) nurtured important questions about online education and its automated assessment (Blackmon & Major, 2017 ; White, 2014 ). Education providers such as Coursera, edX, and Udacity dominantly apply so-called auto-graded assessments (e.g., single- or multiple-choice assessments). Implementing automated scoring for open-ended assessments is still on the agenda of such provides, however, not fully developed yet (Corbeil, Khan, & Corbeil, 2018 ).

With the increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings, further opportunities arise for advancing AES systems in open, distance, and digital education. Analytics-enhanced assessment enlarges standard methods of AES systems through harnessing formative as well as summative data from learners and their contexts in order to facilitate learning processes in near real-time and help decision-makers to improve learning environments. Hence, analytics-enhanced assessment may provide multiple benefits for students, schools, and involved stakeholders. However, as noted by Ellis ( 2013 ), analytics currently fail to make full use of educational data for assessment.

Interest in collecting and mining large sets of educational data on student background and performance has grown over the past years and is generally referred to as learning analytics (R. S. Baker & Siemens, 2015 ). In recent years, the incorporation of learning analytics into educational practices and research has further developed. However, while new applications and approaches have brought forth new insights, there is still a shortage of research addressing the effectiveness and consequences with regard to AES systems. Learning analytics, which refers to the use of static and dynamic data from learners and their contexts for (1) the understanding of learning and the discovery of traces of learning and (2) the support of learning processes and educational decision-making (Ifenthaler, 2015 ), offers a range of opportunities for formative and summative assessment of written text. Hence, the primary goal of learning analytics is to better meet students’ needs by offering individual learning paths, adaptive assessments and recommendations, or adaptive and just-in-time feedback (Gašević, Dawson, & Siemens, 2015 ; McLoughlin & Lee, 2010 ), ideally, tailored to learners’ motivational states, individual characteristics, and learning goals (Schumacher & Ifenthaler, 2018 ). From an assessment perspective focusing on AES systems, learning analytics for formative assessment focuses on the generation and interpretation of evidence about learner performance by teachers, learners, and/or technology to make assisted decisions about the next steps in learning and instruction (Ifenthaler, Greiff, & Gibson, 2018 ; Spector et al., 2016 ). In this context, real- or near-time data are extremely valuable because of their benefits in ongoing learning interactions. Learning analytics for written text from a summative assessment perspective is utilized to make judgments that are typically based on standards or benchmarks (Black & Wiliam, 1998 ).

In conclusion, analytics-enhanced assessments of written essays may reveal personal information and insights into an individual learning history; however, they are not accredited and far from being unbiased, comprehensive, and fully valid at this point in time. Much remains to be done to mitigate these shortcomings in a way that learners will truly benefit from AES systems.

Cross-References

Artificial Intelligence in Education and Ethics

Evolving Learner Support Systems

Introduction to Design, Delivery, and Assessment in ODDE

Learning Analytics in Open, Distance, and Digital Education (ODDE)

Ajay, H. B., Tillett, P. I., & Page, E. B. (1973). The analysis of essays by computer (AEC-II). Final report . Storrs, CT: University of Connecticut.

Google Scholar  

Attali, Y. (2011). A differential word use measure for content analysis in automated essay scoring . ETS Research Report Series , 36.

Attali, Y. (2013). Validity and reliability of automated essay scoring. In M. D. Shermis & J. Burstein (Eds.), Handbook of automated essay evaluation: Current applications and new directions (pp. 181–198). New York, NY: Routledge.

Attali, Y., & Burstein, J. (2006). Automated essay scoring with e-rater V. 2. The Journal of Technology, Learning and Assessment, 4 (3), 3–29. https://doi.org/10.1002/j.2333-8504.2004.tb01972.x .

Article   Google Scholar  

Azmi, A., Al-Jouie, M. F., & Hussain, M. (2019). AAEE – Automated evaluation of students‘ essays in Arabic language. Information Processing & Management, 56 (5), 1736–1752. https://doi.org/10.1016/j.ipm.2019.05.008 .

Baker, E., Chung, G., & Cai, L. (2016). Assessment, gaze, refraction, and blur: The course of achievement testing in the past 100 years. Review of Research in Education, 40 , 94–142. https://doi.org/10.3102/0091732X16679806 .

Baker, R. S., & Siemens, G. (2015). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 253–272). Cambridge, UK: Cambridge University Press.

Bellotti, F., Kapralos, B., Lee, K., Moreno-Ger, P., & Berta, R. (2013). Assessment in and of serious games: An overview. Advances in Human-Computer Interaction, 2013 , 136864. https://doi.org/10.1155/2013/136864 .

Bennett, R. E. (2015). The changing nature of educational assessment. Review of Research in Education, 39 (1), 370–407. https://doi.org/10.3102/0091732x14554179 .

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5 (1), 7–74. https://doi.org/10.1080/0969595980050102 .

Blackmon, S. J., & Major, C. H. (2017). Wherefore art thou MOOC?: Defining massive open online courses. Online Learning Journal, 21 (4), 195–221. https://doi.org/10.24059/olj.v21i4.1272 .

Brill, E. (1995). Unsupervised learning of dismabiguation rules for part of speech tagging. Paper presented at the Second Workshop on Very Large Corpora, WVLC-95, Boston. Paper presentation retrieved from

Burrows, S., Gurevych, I., & Stein, B. (2015). The eras and trends of automatic short answer grading. International Journal of Artificial Intelligence in Education, 25 (1), 60–117. https://doi.org/10.1007/s40593-014-0026-8 .

Corbalan, G., Kester, L., & van Merriënboer, J. J. G. (2009). Dynamic task selection: Effects of feedback and learner control on efficiency and motivation. Learning and Instruction, 19 (6), 455–465. https://doi.org/10.1016/j.learninstruc.2008.07.002 .

Corbeil, J. R., Khan, B. H., & Corbeil, M. E. (2018). MOOCs revisited: Still transformative or passing fad? Asian Journal of University Education, 14 (2), 1–12.

Dikli, S. (2011). The nature of automated essay scoring feedback. CALICO Journal, 28 (1), 99–134. https://doi.org/10.11139/cj.28.1.99-134 .

DuBay, W. H. (2007). Smart language: Readers, readability, and the grading of text . Costa Mesa, CA, USA: BookSurge Publishing.

Elliot, N., & Williamson, D. M. (2013). Assessing writing special issue: Assessing writing with automated scoring systems. Assessing Writing, 18 (1), 1–6. https://doi.org/10.1016/j.asw.2012.11.002 .

Ellis, C. (2013). Broadening the scope and increasing usefulness of learning analytics: The case for assessment analytics. British Journal of Educational Technology, 44 (4), 662–664. https://doi.org/10.1111/bjet.12028 .

Flower, L., & Hayes, J. (1981). A cognitive process theory of writing. College Composition and Communication, 32 (4), 365–387.

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59 (1), 64–71. https://doi.org/10.1007/s11528-014-0822-x .

Gierl, M. J., Latifi, S., Lai, H., Boulais, A.-P., & Champlain, A. (2014). Automated essay scoring and the future of educational assessment in medical education. Medical Education, 48 (10), 950–962. https://doi.org/10.1111/medu.12517 .

Glavas, G., Ganesh, A., & Somasundaran, S. (2021). Training and domain adaptation for supervised text segmentation. Paper presented at the Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, Virtual Conference.

Hartley, R. T., & Barnden, J. A. (1997). Semantic networks: Visualizations of knowledge. Trends in Cognitive Science, 1 (5), 169–175. https://doi.org/10.1016/S1364-6613(97)01057-7 .

Ifenthaler, D. (2014). AKOVIA: Automated knowledge visualization and assessment. Technology, Knowledge and Learning, 19 (1–2), 241–248. https://doi.org/10.1007/s10758-014-9224-6 .

Ifenthaler, D. (2015). Learning analytics. In J. M. Spector (Ed.), The SAGE encyclopedia of educational technology (Vol. 2, pp. 447–451). Thousand Oaks, CA: Sage.

Ifenthaler, D. (2016). Automated grading. In S. Danver (Ed.), The SAGE encyclopedia of online education (p. 130). Thousand Oaks, CA: Sage.

Ifenthaler, D., & Dikli, S. (2015). Automated scoring of essays. In J. M. Spector (Ed.), The SAGE encyclopedia of educational technology (Vol. 1, pp. 64–68). Thousand Oaks, CA: Sage.

Ifenthaler, D., & Greiff, S. (2021). Leveraging learning analytics for assessment and feedback. In J. Liebowitz (Ed.), Online learning analytics (pp. 1–18). Boca Raton, FL: Auerbach Publications.

Ifenthaler, D., Greiff, S., & Gibson, D. C. (2018). Making use of data for assessments: Harnessing analytics and data science. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.), International handbook of IT in primary and secondary education (2nd ed., pp. 649–663). New York, NY: Springer.

Johnson, W. L., & Lester, J. C. (2016). Face-to-face interaction with pedagogical agents, twenty years later. International Journal of Artificial Intelligence in Education, 26 (1), 25–36. https://doi.org/10.1007/s40593-015-0065-9 .

Kawate-Mierzejewska, M. (2003). E-rater software . Paper presented at the Japanese Association for Language Teaching, Tokyo, Japan. Paper presentation retrieved from

Kim, Y. J., & Ifenthaler, D. (2019). Game-based assessment: The past ten years and moving forward. In D. Ifenthaler & Y. J. Kim (Eds.), Game-based assessment revisted (pp. 3–12). Cham, Switzerland: Springer.

Chapter   Google Scholar  

Kumar, V. S., & Boulanger, D. (2020). Automated essay scoring and the deep learning black box: How are rubric scores determined? International Journal of Artificial Intelligence in Education . https://doi.org/10.1007/s40593-020-00211-5 .

Landauer, T. K., Laham, D., & Foltz, P. W. (2003). Automated scoring and annotation of essays with the intelligent essay assessor. In M. D. Shermis & J. Burstein (Eds.), Automated essay scoring: A cross-disciplinary perspective (pp. 87–112). Mahwah, NJ: Erlbaum.

Lehmann, T., Haehnlein, I., & Ifenthaler, D. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32 , 313–323. https://doi.org/10.1016/j.chb.2013.07.051 .

McLoughlin, C., & Lee, M. J. W. (2010). Personalized and self regulated learning in the Web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26 (1), 28–43.

Page, E. B. (1966). The imminence of grading essays by computer. Phi Delta Kappan, 47 (5), 238–243.

Page, E. B. (1968). The use of the computer in analyzing student essays. International Review of Education, 14 (2), 210–225. https://doi.org/10.1007/BF01419938 .

Pirnay-Dummer, P., & Ifenthaler, D. (2010). Automated knowledge visualization and assessment. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 77–115). New York, NY: Springer.

Pirnay-Dummer, P., & Ifenthaler, D. (2011). Text-guided automated self assessment. A graph-based approach to help learners with ongoing writing. In D. Ifenthaler, K. P. Isaias, D. G. Sampson, & J. M. Spector (Eds.), Multiple perspectives on problem solving and learning in the digital age (pp. 217–225). New York, NY: Springer.

Pirnay-Dummer, P., Ifenthaler, D., & Seel, N. M. (2012). Semantic networks. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (Vol. 19, pp. 3025–3029). New York, NY: Springer.

Ramesh, D., & Sanampudi, S. K. (2021). An automated essay scoring systems: A systematic literature review. Artificial Intelligence Review . https://doi.org/10.1007/s10462-021-10068-2 .

Ramineni, C., & Williamson, D. M. (2013). Automated essay scoring: Psychometric guidelines and practices. Assessing Writing, 18 (1), 25–39. https://doi.org/10.1016/j.asw.2012.10.004 .

Rissanen, M. J., Kume, N., Kuroda, Y., Kuroda, T., Yoshimura, K., & Yoshihara, H. (2008). Asynchronous teaching of psychomotor skills through VR annotations: Evaluation in digital rectal examination. Studies in Health Technology and Informatics, 132 , 411–416.

Schumacher, C., & Ifenthaler, D. (2018). The importance of students’ motivational dispositions for designing learning analytics. Journal of Computing in Higher Education, 30 (3), 599–619. https://doi.org/10.1007/s12528-018-9188-y .

Schumacher, C., & Ifenthaler, D. (2021). Investigating prompts for supporting students’ self-regulation – A remaining challenge for learning analytics approaches? The Internet and Higher Education, 49 , 100791. https://doi.org/10.1016/j.iheduc.2020.100791 .

Shermis, M. D. (2010). Automated essay scoring in a high stakes testing environment. In V. J. Shute & B. J. Becker (Eds.), Innovative assessment for the 21st century (pp. 167–184). New York, NY: Springer.

Shermis, M. D., Burstein, J., Higgins, D., & Zechner, K. (2010). Automated essay scoring: Writing assessment and instruction. In P. Petersen, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (pp. 75–80). Oxford, England: Elsevier.

Spector, J. M., Ifenthaler, D., Sampson, D. G., Yang, L., Mukama, E., Warusavitarana, A., … Gibson, D. C. (2016). Technology enhanced formative assessment for 21st century learning. Educational Technology & Society, 19 (3), 58–71.

Stephen, T. C., Gierl, M. C., & King, S. (2021). Automated essay scoring (AES) of constructed responses in nursing examinations: An evaluation. Nurse Education in Practice, 54 , 103085. https://doi.org/10.1016/j.nepr.2021.103085 .

Stödberg, U. (2012). A research review of e-assessment. Assessment & Evaluation in Higher Education, 37 (5), 591–604. https://doi.org/10.1080/02602938.2011.557496 .

Tversky, A. (1977). Features of similarity. Psychological Review, 84 , 327–352.

Valenti, S., Neri, F., & Cucchiarelli, A. (2003). An overview of current research on automated essay grading. Journal of Information Technology Education, 2 , 319–330.

Vantage Learning. (2001). A preliminary study of the efficacy of IntelliMetric ® for use in scoring Hebrew assessments . Retrieved from Newtown, PA:

Vantage Learning. (2002). A study of IntelliMetric ® scoring for responses written in Bahasa Malay (No. RB-735) . Retrieved from Newtown, PA:

Webb, M., Gibson, D. C., & Forkosh-Baruch, A. (2013). Challenges for information technology supporting educational assessment. Journal of Computer Assisted Learning, 29 (5), 451–462. https://doi.org/10.1111/jcal.12033 .

Webb, M., & Ifenthaler, D. (2018). Section introduction: Using information technology for assessment: Issues and opportunities. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.), International handbook of IT in primary and secondary education (2nd ed., pp. 577–580). Cham, Switzerland: Springer.

White, B. (2014). Is “MOOC-mania” over? In S. S. Cheung, J. Fong, J. Zhang, R. Kwan, & L. Kwok (Eds.), Hybrid learning. Theory and practice (Vol. 8595, pp. 11–15). Cham, Switzerland: Springer International Publishing.

Whitelock, D., & Bektik, D. (2018). Progress and challenges for automated scoring and feedback systems for large-scale assessments. In J. Voogt, G. Knezek, R. Christensen, & K.-W. Lai (Eds.), International handbook of IT in primary and secondary education (2nd ed., pp. 617–634). New York, NY: Springer.

Wild, F. (2016). Learning analytics in R with SNA, LSA, and MPIA . Heidelberg, Germany: Springer.

Book   Google Scholar  

Wilson, J., & Rodrigues, J. (2020). Classification accuracy and efficiency of writing screening using automated essay scoring. Journal of School Psychology, 82 , 123–140. https://doi.org/10.1016/j.jsp.2020.08.008 .

Zupanc, K., & Bosnic, Z. (2015). Advances in the field of automated essay evaluation. Informatica, 39 (4), 383–395.

Zupanc, K., & Bosnic, Z. (2017). Automated essay evaluation with semantic analysis. Knowledge-Based Systems, 120 , 118–132. https://doi.org/10.1016/j.knosys.2017.01.006 .

Download references

Author information

Authors and affiliations.

University of Mannheim, Mannheim, Germany

Dirk Ifenthaler

Curtin University, Perth, WA, Australia

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Dirk Ifenthaler .

Editor information

Editors and affiliations.

Center of Open Education Research, Carl von Ossietzky University of Oldenburg, Oldenburg, Niedersachsen, Germany

Olaf Zawacki-Richter

Education Research Institute, Seoul National University, Seoul, Korea (Republic of)

Insung Jung

Section Editor information

Florida State University, Tallahassee, FL, USA

Vanessa Dennen

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2023 The Author(s)

About this entry

Cite this entry.

Ifenthaler, D. (2023). Automated Essay Scoring Systems. In: Zawacki-Richter, O., Jung, I. (eds) Handbook of Open, Distance and Digital Education. Springer, Singapore. https://doi.org/10.1007/978-981-19-2080-6_59

Download citation

DOI : https://doi.org/10.1007/978-981-19-2080-6_59

Published : 01 January 2023

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-2079-0

Online ISBN : 978-981-19-2080-6

eBook Packages : Education Reference Module Humanities and Social Sciences Reference Module Education

Share this entry

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

An automated essay scoring systems: a systematic literature review

Dadi ramesh.

1 School of Computer Science and Artificial Intelligence, SR University, Warangal, TS India

2 Research Scholar, JNTU, Hyderabad, India

Suresh Kumar Sanampudi

3 Department of Information Technology, JNTUH College of Engineering, Nachupally, Kondagattu, Jagtial, TS India

Associated Data

Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-021-10068-2.

Introduction

Due to COVID 19 outbreak, an online educational system has become inevitable. In the present scenario, almost all the educational institutions ranging from schools to colleges adapt the online education system. The assessment plays a significant role in measuring the learning ability of the student. Most automated evaluation is available for multiple-choice questions, but assessing short and essay answers remain a challenge. The education system is changing its shift to online-mode, like conducting computer-based exams and automatic evaluation. It is a crucial application related to the education domain, which uses natural language processing (NLP) and Machine Learning techniques. The evaluation of essays is impossible with simple programming languages and simple techniques like pattern matching and language processing. Here the problem is for a single question, we will get more responses from students with a different explanation. So, we need to evaluate all the answers concerning the question.

Automated essay scoring (AES) is a computer-based assessment system that automatically scores or grades the student responses by considering appropriate features. The AES research started in 1966 with the Project Essay Grader (PEG) by Ajay et al. ( 1973 ). PEG evaluates the writing characteristics such as grammar, diction, construction, etc., to grade the essay. A modified version of the PEG by Shermis et al. ( 2001 ) was released, which focuses on grammar checking with a correlation between human evaluators and the system. Foltz et al. ( 1999 ) introduced an Intelligent Essay Assessor (IEA) by evaluating content using latent semantic analysis to produce an overall score. Powers et al. ( 2002 ) proposed E-rater and Intellimetric by Rudner et al. ( 2006 ) and Bayesian Essay Test Scoring System (BESTY) by Rudner and Liang ( 2002 ), these systems use natural language processing (NLP) techniques that focus on style and content to obtain the score of an essay. The vast majority of the essay scoring systems in the 1990s followed traditional approaches like pattern matching and a statistical-based approach. Since the last decade, the essay grading systems started using regression-based and natural language processing techniques. AES systems like Dong et al. ( 2017 ) and others developed from 2014 used deep learning techniques, inducing syntactic and semantic features resulting in better results than earlier systems.

Ohio, Utah, and most US states are using AES systems in school education, like Utah compose tool, Ohio standardized test (an updated version of PEG), evaluating millions of student's responses every year. These systems work for both formative, summative assessments and give feedback to students on the essay. Utah provided basic essay evaluation rubrics (six characteristics of essay writing): Development of ideas, organization, style, word choice, sentence fluency, conventions. Educational Testing Service (ETS) has been conducting significant research on AES for more than a decade and designed an algorithm to evaluate essays on different domains and providing an opportunity for test-takers to improve their writing skills. In addition, they are current research content-based evaluation.

The evaluation of essay and short answer scoring should consider the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge. Proper assessment of the parameters mentioned above defines the accuracy of the evaluation system. But all these parameters cannot play an equal role in essay scoring and short answer scoring. In a short answer evaluation, domain knowledge is required, like the meaning of "cell" in physics and biology is different. And while evaluating essays, the implementation of ideas with respect to prompt is required. The system should also assess the completeness of the responses and provide feedback.

Several studies examined AES systems, from the initial to the latest AES systems. In which the following studies on AES systems are Blood ( 2011 ) provided a literature review from PEG 1984–2010. Which has covered only generalized parts of AES systems like ethical aspects, the performance of the systems. Still, they have not covered the implementation part, and it’s not a comparative study and has not discussed the actual challenges of AES systems.

Burrows et al. ( 2015 ) Reviewed AES systems on six dimensions like dataset, NLP techniques, model building, grading models, evaluation, and effectiveness of the model. They have not covered feature extraction techniques and challenges in features extractions. Covered only Machine Learning models but not in detail. This system not covered the comparative analysis of AES systems like feature extraction, model building, and level of relevance, cohesion, and coherence not covered in this review.

Ke et al. ( 2019 ) provided a state of the art of AES system but covered very few papers and not listed all challenges, and no comparative study of the AES model. On the other hand, Hussein et al. in ( 2019 ) studied two categories of AES systems, four papers from handcrafted features for AES systems, and four papers from the neural networks approach, discussed few challenges, and did not cover feature extraction techniques, the performance of AES models in detail.

Klebanov et al. ( 2020 ). Reviewed 50 years of AES systems, listed and categorized all essential features that need to be extracted from essays. But not provided a comparative analysis of all work and not discussed the challenges.

This paper aims to provide a systematic literature review (SLR) on automated essay grading systems. An SLR is an Evidence-based systematic review to summarize the existing research. It critically evaluates and integrates all relevant studies' findings and addresses the research domain's specific research questions. Our research methodology uses guidelines given by Kitchenham et al. ( 2009 ) for conducting the review process; provide a well-defined approach to identify gaps in current research and to suggest further investigation.

We addressed our research method, research questions, and the selection process in Sect.  2 , and the results of the research questions have discussed in Sect.  3 . And the synthesis of all the research questions addressed in Sect.  4 . Conclusion and possible future work discussed in Sect.  5 .

Research method

We framed the research questions with PICOC criteria.

Population (P) Student essays and answers evaluation systems.

Intervention (I) evaluation techniques, data sets, features extraction methods.

Comparison (C) Comparison of various approaches and results.

Outcomes (O) Estimate the accuracy of AES systems,

Context (C) NA.

Research questions

To collect and provide research evidence from the available studies in the domain of automated essay grading, we framed the following research questions (RQ):

RQ1 what are the datasets available for research on automated essay grading?

The answer to the question can provide a list of the available datasets, their domain, and access to the datasets. It also provides a number of essays and corresponding prompts.

RQ2 what are the features extracted for the assessment of essays?

The answer to the question can provide an insight into various features so far extracted, and the libraries used to extract those features.

RQ3, which are the evaluation metrics available for measuring the accuracy of algorithms?

The answer will provide different evaluation metrics for accurate measurement of each Machine Learning approach and commonly used measurement technique.

RQ4 What are the Machine Learning techniques used for automatic essay grading, and how are they implemented?

It can provide insights into various Machine Learning techniques like regression models, classification models, and neural networks for implementing essay grading systems. The response to the question can give us different assessment approaches for automated essay grading systems.

RQ5 What are the challenges/limitations in the current research?

The answer to the question provides limitations of existing research approaches like cohesion, coherence, completeness, and feedback.

Search process

We conducted an automated search on well-known computer science repositories like ACL, ACM, IEEE Explore, Springer, and Science Direct for an SLR. We referred to papers published from 2010 to 2020 as much of the work during these years focused on advanced technologies like deep learning and natural language processing for automated essay grading systems. Also, the availability of free data sets like Kaggle (2012), Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) by Yannakoudakis et al. ( 2011 ) led to research this domain.

Search Strings : We used search strings like “Automated essay grading” OR “Automated essay scoring” OR “short answer scoring systems” OR “essay scoring systems” OR “automatic essay evaluation” and searched on metadata.

Selection criteria

After collecting all relevant documents from the repositories, we prepared selection criteria for inclusion and exclusion of documents. With the inclusion and exclusion criteria, it becomes more feasible for the research to be accurate and specific.

Inclusion criteria 1 Our approach is to work with datasets comprise of essays written in English. We excluded the essays written in other languages.

Inclusion criteria 2  We included the papers implemented on the AI approach and excluded the traditional methods for the review.

Inclusion criteria 3 The study is on essay scoring systems, so we exclusively included the research carried out on only text data sets rather than other datasets like image or speech.

Exclusion criteria  We removed the papers in the form of review papers, survey papers, and state of the art papers.

Quality assessment

In addition to the inclusion and exclusion criteria, we assessed each paper by quality assessment questions to ensure the article's quality. We included the documents that have clearly explained the approach they used, the result analysis and validation.

The quality checklist questions are framed based on the guidelines from Kitchenham et al. ( 2009 ). Each quality assessment question was graded as either 1 or 0. The final score of the study range from 0 to 3. A cut off score for excluding a study from the review is 2 points. Since the papers scored 2 or 3 points are included in the final evaluation. We framed the following quality assessment questions for the final study.

Quality Assessment 1: Internal validity.

Quality Assessment 2: External validity.

Quality Assessment 3: Bias.

The two reviewers review each paper to select the final list of documents. We used the Quadratic Weighted Kappa score to measure the final agreement between the two reviewers. The average resulted from the kappa score is 0.6942, a substantial agreement between the reviewers. The result of evolution criteria shown in Table ​ Table1. 1 . After Quality Assessment, the final list of papers for review is shown in Table ​ Table2. 2 . The complete selection process is shown in Fig. ​ Fig.1. 1 . The total number of selected papers in year wise as shown in Fig. ​ Fig.2. 2 .

Quality assessment analysis

Final list of papers

Selection process

Year wise publications

What are the datasets available for research on automated essay grading?

To work with problem statement especially in Machine Learning and deep learning domain, we require considerable amount of data to train the models. To answer this question, we listed all the data sets used for training and testing for automated essay grading systems. The Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) Yannakoudakis et al. ( 2011 ) developed corpora that contain 1244 essays and ten prompts. This corpus evaluates whether a student can write the relevant English sentences without any grammatical and spelling mistakes. This type of corpus helps to test the models built for GRE and TOFEL type of exams. It gives scores between 1 and 40.

Bailey and Meurers ( 2008 ), Created a dataset (CREE reading comprehension) for language learners and automated short answer scoring systems. The corpus consists of 566 responses from intermediate students. Mohler and Mihalcea ( 2009 ). Created a dataset for the computer science domain consists of 630 responses for data structure assignment questions. The scores are range from 0 to 5 given by two human raters.

Dzikovska et al. ( 2012 ) created a Student Response Analysis (SRA) corpus. It consists of two sub-groups: the BEETLE corpus consists of 56 questions and approximately 3000 responses from students in the electrical and electronics domain. The second one is the SCIENTSBANK(SemEval-2013) (Dzikovska et al. 2013a ; b ) corpus consists of 10,000 responses on 197 prompts on various science domains. The student responses ladled with "correct, partially correct incomplete, Contradictory, Irrelevant, Non-domain."

In the Kaggle (2012) competition, released total 3 types of corpuses on an Automated Student Assessment Prize (ASAP1) (“ https://www.kaggle.com/c/asap-sas/ ” ) essays and short answers. It has nearly 17,450 essays, out of which it provides up to 3000 essays for each prompt. It has eight prompts that test 7th to 10th grade US students. It gives scores between the [0–3] and [0–60] range. The limitations of these corpora are: (1) it has a different score range for other prompts. (2) It uses statistical features such as named entities extraction and lexical features of words to evaluate essays. ASAP +  + is one more dataset from Kaggle. It is with six prompts, and each prompt has more than 1000 responses total of 10,696 from 8th-grade students. Another corpus contains ten prompts from science, English domains and a total of 17,207 responses. Two human graders evaluated all these responses.

Correnti et al. ( 2013 ) created a Response-to-Text Assessment (RTA) dataset used to check student writing skills in all directions like style, mechanism, and organization. 4–8 grade students give the responses to RTA. Basu et al. ( 2013 ) created a power grading dataset with 700 responses for ten different prompts from US immigration exams. It contains all short answers for assessment.

The TOEFL11 corpus Blanchard et al. ( 2013 ) contains 1100 essays evenly distributed over eight prompts. It is used to test the English language skills of a candidate attending the TOFEL exam. It scores the language proficiency of a candidate as low, medium, and high.

International Corpus of Learner English (ICLE) Granger et al. ( 2009 ) built a corpus of 3663 essays covering different dimensions. It has 12 prompts with 1003 essays that test the organizational skill of essay writing, and13 prompts, each with 830 essays that examine the thesis clarity and prompt adherence.

Argument Annotated Essays (AAE) Stab and Gurevych ( 2014 ) developed a corpus that contains 102 essays with 101 prompts taken from the essayforum2 site. It tests the persuasive nature of the student essay. The SCIENTSBANK corpus used by Sakaguchi et al. ( 2015 ) available in git-hub, containing 9804 answers to 197 questions in 15 science domains. Table ​ Table3 3 illustrates all datasets related to AES systems.

ALL types Datasets used in Automatic scoring systems

Features play a major role in the neural network and other supervised Machine Learning approaches. The automatic essay grading systems scores student essays based on different types of features, which play a prominent role in training the models. Based on their syntax and semantics and they are categorized into three groups. 1. statistical-based features Contreras et al. ( 2018 ); Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ) 2. Style-based (Syntax) features Cummins et al. ( 2016 ); Darwish and Mohamed ( 2020 ); Ke et al. ( 2019 ). 3. Content-based features Dong et al. ( 2017 ). A good set of features appropriate models evolved better AES systems. The vast majority of the researchers are using regression models if features are statistical-based. For Neural Networks models, researches are using both style-based and content-based features. The following table shows the list of various features used in existing AES Systems. Table ​ Table4 4 represents all set of features used for essay grading.

Types of features

We studied all the feature extracting NLP libraries as shown in Fig. ​ Fig.3. that 3 . that are used in the papers. The NLTK is an NLP tool used to retrieve statistical features like POS, word count, sentence count, etc. With NLTK, we can miss the essay's semantic features. To find semantic features Word2Vec Mikolov et al. ( 2013 ), GloVe Jeffrey Pennington et al. ( 2014 ) is the most used libraries to retrieve the semantic text from the essays. And in some systems, they directly trained the model with word embeddings to find the score. From Fig. ​ Fig.4 4 as observed that non-content-based feature extraction is higher than content-based.

Usages of tools

Number of papers on content based features

RQ3 which are the evaluation metrics available for measuring the accuracy of algorithms?

The majority of the AES systems are using three evaluation metrics. They are (1) quadrated weighted kappa (QWK) (2) Mean Absolute Error (MAE) (3) Pearson Correlation Coefficient (PCC) Shehab et al. ( 2016 ). The quadratic weighted kappa will find agreement between human evaluation score and system evaluation score and produces value ranging from 0 to 1. And the Mean Absolute Error is the actual difference between human-rated score to system-generated score. The mean square error (MSE) measures the average squares of the errors, i.e., the average squared difference between the human-rated and the system-generated scores. MSE will always give positive numbers only. Pearson's Correlation Coefficient (PCC) finds the correlation coefficient between two variables. It will provide three values (0, 1, − 1). "0" represents human-rated and system scores that are not related. "1" represents an increase in the two scores. "− 1" illustrates a negative relationship between the two scores.

RQ4 what are the Machine Learning techniques being used for automatic essay grading, and how are they implemented?

After scrutinizing all documents, we categorize the techniques used in automated essay grading systems into four baskets. 1. Regression techniques. 2. Classification model. 3. Neural networks. 4. Ontology-based approach.

All the existing AES systems developed in the last ten years employ supervised learning techniques. Researchers using supervised methods viewed the AES system as either regression or classification task. The goal of the regression task is to predict the score of an essay. The classification task is to classify the essays belonging to (low, medium, or highly) relevant to the question's topic. Since the last three years, most AES systems developed made use of the concept of the neural network.

Regression based models

Mohler and Mihalcea ( 2009 ). proposed text-to-text semantic similarity to assign a score to the student essays. There are two text similarity measures like Knowledge-based measures, corpus-based measures. There eight knowledge-based tests with all eight models. They found the similarity. The shortest path similarity determines based on the length, which shortest path between two contexts. Leacock & Chodorow find the similarity based on the shortest path's length between two concepts using node-counting. The Lesk similarity finds the overlap between the corresponding definitions, and Wu & Palmer algorithm finds similarities based on the depth of two given concepts in the wordnet taxonomy. Resnik, Lin, Jiang&Conrath, Hirst& St-Onge find the similarity based on different parameters like the concept, probability, normalization factor, lexical chains. In corpus-based likeness, there LSA BNC, LSA Wikipedia, and ESA Wikipedia, latent semantic analysis is trained on Wikipedia and has excellent domain knowledge. Among all similarity scores, correlation scores LSA Wikipedia scoring accuracy is more. But these similarity measure algorithms are not using NLP concepts. These models are before 2010 and basic concept models to continue the research automated essay grading with updated algorithms on neural networks with content-based features.

Adamson et al. ( 2014 ) proposed an automatic essay grading system which is a statistical-based approach in this they retrieved features like POS, Character count, Word count, Sentence count, Miss spelled words, n-gram representation of words to prepare essay vector. They formed a matrix with these all vectors in that they applied LSA to give a score to each essay. It is a statistical approach that doesn’t consider the semantics of the essay. The accuracy they got when compared to the human rater score with the system is 0.532.

Cummins et al. ( 2016 ). Proposed Timed Aggregate Perceptron vector model to give ranking to all the essays, and later they converted the rank algorithm to predict the score of the essay. The model trained with features like Word unigrams, bigrams, POS, Essay length, grammatical relation, Max word length, sentence length. It is multi-task learning, gives ranking to the essays, and predicts the score for the essay. The performance evaluated through QWK is 0.69, a substantial agreement between the human rater and the system.

Sultan et al. ( 2016 ). Proposed a Ridge regression model to find short answer scoring with Question Demoting. Question Demoting is the new concept included in the essay's final assessment to eliminate duplicate words from the essay. The extracted features are Text Similarity, which is the similarity between the student response and reference answer. Question Demoting is the number of repeats in a student response. With inverse document frequency, they assigned term weight. The sentence length Ratio is the number of words in the student response, is another feature. With these features, the Ridge regression model was used, and the accuracy they got 0.887.

Contreras et al. ( 2018 ). Proposed Ontology based on text mining in this model has given a score for essays in phases. In phase-I, they generated ontologies with ontoGen and SVM to find the concept and similarity in the essay. In phase II from ontologies, they retrieved features like essay length, word counts, correctness, vocabulary, and types of word used, domain information. After retrieving statistical data, they used a linear regression model to find the score of the essay. The accuracy score is the average of 0.5.

Darwish and Mohamed ( 2020 ) proposed the fusion of fuzzy Ontology with LSA. They retrieve two types of features, like syntax features and semantic features. In syntax features, they found Lexical Analysis with tokens, and they construct a parse tree. If the parse tree is broken, the essay is inconsistent—a separate grade assigned to the essay concerning syntax features. The semantic features are like similarity analysis, Spatial Data Analysis. Similarity analysis is to find duplicate sentences—Spatial Data Analysis for finding Euclid distance between the center and part. Later they combine syntax features and morphological features score for the final score. The accuracy they achieved with the multiple linear regression model is 0.77, mostly on statistical features.

Süzen Neslihan et al. ( 2020 ) proposed a text mining approach for short answer grading. First, their comparing model answers with student response by calculating the distance between two sentences. By comparing the model answer with student response, they find the essay's completeness and provide feedback. In this approach, model vocabulary plays a vital role in grading, and with this model vocabulary, the grade will be assigned to the student's response and provides feedback. The correlation between the student answer to model answer is 0.81.

Classification based Models

Persing and Ng ( 2013 ) used a support vector machine to score the essay. The features extracted are OS, N-gram, and semantic text to train the model and identified the keywords from the essay to give the final score.

Sakaguchi et al. ( 2015 ) proposed two methods: response-based and reference-based. In response-based scoring, the extracted features are response length, n-gram model, and syntactic elements to train the support vector regression model. In reference-based scoring, features such as sentence similarity using word2vec is used to find the cosine similarity of the sentences that is the final score of the response. First, the scores were discovered individually and later combined two features to find a final score. This system gave a remarkable increase in performance by combining the scores.

Mathias and Bhattacharyya ( 2018a ; b ) Proposed Automated Essay Grading Dataset with Essay Attribute Scores. The first concept features selection depends on the essay type. So the common attributes are Content, Organization, Word Choice, Sentence Fluency, Conventions. In this system, each attribute is scored individually, with the strength of each attribute identified. The model they used is a random forest classifier to assign scores to individual attributes. The accuracy they got with QWK is 0.74 for prompt 1 of the ASAS dataset ( https://www.kaggle.com/c/asap-sas/ ).

Ke et al. ( 2019 ) used a support vector machine to find the response score. In this method, features like Agreeability, Specificity, Clarity, Relevance to prompt, Conciseness, Eloquence, Confidence, Direction of development, Justification of opinion, and Justification of importance. First, the individual parameter score obtained was later combined with all scores to give a final response score. The features are used in the neural network to find whether the sentence is relevant to the topic or not.

Salim et al. ( 2019 ) proposed an XGBoost Machine Learning classifier to assess the essays. The algorithm trained on features like word count, POS, parse tree depth, and coherence in the articles with sentence similarity percentage; cohesion and coherence are considered for training. And they implemented K-fold cross-validation for a result the average accuracy after specific validations is 68.12.

Neural network models

Shehab et al. ( 2016 ) proposed a neural network method that used learning vector quantization to train human scored essays. After training, the network can provide a score to the ungraded essays. First, we should process the essay to remove Spell checking and then perform preprocessing steps like Document Tokenization, stop word removal, Stemming, and submit it to the neural network. Finally, the model will provide feedback on the essay, whether it is relevant to the topic. And the correlation coefficient between human rater and system score is 0.7665.

Kopparapu and De ( 2016 ) proposed the Automatic Ranking of Essays using Structural and Semantic Features. This approach constructed a super essay with all the responses. Next, ranking for a student essay is done based on the super-essay. The structural and semantic features derived helps to obtain the scores. In a paragraph, 15 Structural features like an average number of sentences, the average length of sentences, and the count of words, nouns, verbs, adjectives, etc., are used to obtain a syntactic score. A similarity score is used as semantic features to calculate the overall score.

Dong and Zhang ( 2016 ) proposed a hierarchical CNN model. The model builds two layers with word embedding to represents the words as the first layer. The second layer is a word convolution layer with max-pooling to find word vectors. The next layer is a sentence-level convolution layer with max-pooling to find the sentence's content and synonyms. A fully connected dense layer produces an output score for an essay. The accuracy with the hierarchical CNN model resulted in an average QWK of 0.754.

Taghipour and Ng ( 2016 ) proposed a first neural approach for essay scoring build in which convolution and recurrent neural network concepts help in scoring an essay. The network uses a lookup table with the one-hot representation of the word vector of an essay. The final efficiency of the network model with LSTM resulted in an average QWK of 0.708.

Dong et al. ( 2017 ). Proposed an Attention-based scoring system with CNN + LSTM to score an essay. For CNN, the input parameters were character embedding and word embedding, and it has attention pooling layers and used NLTK to obtain word and character embedding. The output gives a sentence vector, which provides sentence weight. After CNN, it will have an LSTM layer with an attention pooling layer, and this final layer results in the final score of the responses. The average QWK score is 0.764.

Riordan et al. ( 2017 ) proposed a neural network with CNN and LSTM layers. Word embedding, given as input to a neural network. An LSTM network layer will retrieve the window features and delivers them to the aggregation layer. The aggregation layer is a superficial layer that takes a correct window of words and gives successive layers to predict the answer's sore. The accuracy of the neural network resulted in a QWK of 0.90.

Zhao et al. ( 2017 ) proposed a new concept called Memory-Augmented Neural network with four layers, input representation layer, memory addressing layer, memory reading layer, and output layer. An input layer represents all essays in a vector form based on essay length. After converting the word vector, the memory addressing layer takes a sample of the essay and weighs all the terms. The memory reading layer takes the input from memory addressing segment and finds the content to finalize the score. Finally, the output layer will provide the final score of the essay. The accuracy of essay scores is 0.78, which is far better than the LSTM neural network.

Mathias and Bhattacharyya ( 2018a ; b ) proposed deep learning networks using LSTM with the CNN layer and GloVe pre-trained word embeddings. For this, they retrieved features like Sentence count essays, word count per sentence, Number of OOVs in the sentence, Language model score, and the text's perplexity. The network predicted the goodness scores of each essay. The higher the goodness scores, means higher the rank and vice versa.

Nguyen and Dery ( 2016 ). Proposed Neural Networks for Automated Essay Grading. In this method, a single layer bi-directional LSTM accepting word vector as input. Glove vectors used in this method resulted in an accuracy of 90%.

Ruseti et al. ( 2018 ) proposed a recurrent neural network that is capable of memorizing the text and generate a summary of an essay. The Bi-GRU network with the max-pooling layer molded on the word embedding of each document. It will provide scoring to the essay by comparing it with a summary of the essay from another Bi-GRU network. The result obtained an accuracy of 0.55.

Wang et al. ( 2018a ; b ) proposed an automatic scoring system with the bi-LSTM recurrent neural network model and retrieved the features using the word2vec technique. This method generated word embeddings from the essay words using the skip-gram model. And later, word embedding is used to train the neural network to find the final score. The softmax layer in LSTM obtains the importance of each word. This method used a QWK score of 0.83%.

Dasgupta et al. ( 2018 ) proposed a technique for essay scoring with augmenting textual qualitative Features. It extracted three types of linguistic, cognitive, and psychological features associated with a text document. The linguistic features are Part of Speech (POS), Universal Dependency relations, Structural Well-formedness, Lexical Diversity, Sentence Cohesion, Causality, and Informativeness of the text. The psychological features derived from the Linguistic Information and Word Count (LIWC) tool. They implemented a convolution recurrent neural network that takes input as word embedding and sentence vector, retrieved from the GloVe word vector. And the second layer is the Convolution Layer to find local features. The next layer is the recurrent neural network (LSTM) to find corresponding of the text. The accuracy of this method resulted in an average QWK of 0.764.

Liang et al. ( 2018 ) proposed a symmetrical neural network AES model with Bi-LSTM. They are extracting features from sample essays and student essays and preparing an embedding layer as input. The embedding layer output is transfer to the convolution layer from that LSTM will be trained. Hear the LSRM model has self-features extraction layer, which will find the essay's coherence. The average QWK score of SBLSTMA is 0.801.

Liu et al. ( 2019 ) proposed two-stage learning. In the first stage, they are assigning a score based on semantic data from the essay. The second stage scoring is based on some handcrafted features like grammar correction, essay length, number of sentences, etc. The average score of the two stages is 0.709.

Pedro Uria Rodriguez et al. ( 2019 ) proposed a sequence-to-sequence learning model for automatic essay scoring. They used BERT (Bidirectional Encoder Representations from Transformers), which extracts the semantics from a sentence from both directions. And XLnet sequence to sequence learning model to extract features like the next sentence in an essay. With this pre-trained model, they attained coherence from the essay to give the final score. The average QWK score of the model is 75.5.

Xia et al. ( 2019 ) proposed a two-layer Bi-directional LSTM neural network for the scoring of essays. The features extracted with word2vec to train the LSTM and accuracy of the model in an average of QWK is 0.870.

Kumar et al. ( 2019 ) Proposed an AutoSAS for short answer scoring. It used pre-trained Word2Vec and Doc2Vec models trained on Google News corpus and Wikipedia dump, respectively, to retrieve the features. First, they tagged every word POS and they found weighted words from the response. It also found prompt overlap to observe how the answer is relevant to the topic, and they defined lexical overlaps like noun overlap, argument overlap, and content overlap. This method used some statistical features like word frequency, difficulty, diversity, number of unique words in each response, type-token ratio, statistics of the sentence, word length, and logical operator-based features. This method uses a random forest model to train the dataset. The data set has sample responses with their associated score. The model will retrieve the features from both responses like graded and ungraded short answers with questions. The accuracy of AutoSAS with QWK is 0.78. It will work on any topics like Science, Arts, Biology, and English.

Jiaqi Lun et al. ( 2020 ) proposed an automatic short answer scoring with BERT. In this with a reference answer comparing student responses and assigning scores. The data augmentation is done with a neural network and with one correct answer from the dataset classifying reaming responses as correct or incorrect.

Zhu and Sun ( 2020 ) proposed a multimodal Machine Learning approach for automated essay scoring. First, they count the grammar score with the spaCy library and numerical count as the number of words and sentences with the same library. With this input, they trained a single and Bi LSTM neural network for finding the final score. For the LSTM model, they prepared sentence vectors with GloVe and word embedding with NLTK. Bi-LSTM will check each sentence in both directions to find semantic from the essay. The average QWK score with multiple models is 0.70.

Ontology based approach

Mohler et al. ( 2011 ) proposed a graph-based method to find semantic similarity in short answer scoring. For the ranking of answers, they used the support vector regression model. The bag of words is the main feature extracted in the system.

Ramachandran et al. ( 2015 ) also proposed a graph-based approach to find lexical based semantics. Identified phrase patterns and text patterns are the features to train a random forest regression model to score the essays. The accuracy of the model in a QWK is 0.78.

Zupanc et al. ( 2017 ) proposed sentence similarity networks to find the essay's score. Ajetunmobi and Daramola ( 2017 ) recommended an ontology-based information extraction approach and domain-based ontology to find the score.

Speech response scoring

Automatic scoring is in two ways one is text-based scoring, other is speech-based scoring. This paper discussed text-based scoring and its challenges, and now we cover speech scoring and common points between text and speech-based scoring. Evanini and Wang ( 2013 ), Worked on speech scoring of non-native school students, extracted features with speech ratter, and trained a linear regression model, concluding that accuracy varies based on voice pitching. Loukina et al. ( 2015 ) worked on feature selection from speech data and trained SVM. Malinin et al. ( 2016 ) used neural network models to train the data. Loukina et al. ( 2017 ). Proposed speech and text-based automatic scoring. Extracted text-based features, speech-based features and trained a deep neural network for speech-based scoring. They extracted 33 types of features based on acoustic signals. Malinin et al. ( 2017 ). Wu Xixin et al. ( 2020 ) Worked on deep neural networks for spoken language assessment. Incorporated different types of models and tested them. Ramanarayanan et al. ( 2017 ) worked on feature extraction methods and extracted punctuation, fluency, and stress and trained different Machine Learning models for scoring. Knill et al. ( 2018 ). Worked on Automatic speech recognizer and its errors how its impacts the speech assessment.

The state of the art

This section provides an overview of the existing AES systems with a comparative study w. r. t models, features applied, datasets, and evaluation metrics used for building the automated essay grading systems. We divided all 62 papers into two sets of the first set of review papers in Table ​ Table5 5 with a comparative study of the AES systems.

State of the art

Comparison of all approaches

In our study, we divided major AES approaches into three categories. Regression models, classification models, and neural network models. The regression models failed to find cohesion and coherence from the essay because it trained on BoW(Bag of Words) features. In processing data from input to output, the regression models are less complicated than neural networks. There are unable to find many intricate patterns from the essay and unable to find sentence connectivity. If we train the model with BoW features in the neural network approach, the model never considers the essay's coherence and coherence.

First, to train a Machine Learning algorithm with essays, all the essays are converted to vector form. We can form a vector with BoW and Word2vec, TF-IDF. The BoW and Word2vec vector representation of essays represented in Table ​ Table6. 6 . The vector representation of BoW with TF-IDF is not incorporating the essays semantic, and it’s just statistical learning from a given vector. Word2vec vector comprises semantic of essay in a unidirectional way.

Vector representation of essays

In BoW, the vector contains the frequency of word occurrences in the essay. The vector represents 1 and more based on the happenings of words in the essay and 0 for not present. So, in BoW, the vector does not maintain the relationship with adjacent words; it’s just for single words. In word2vec, the vector represents the relationship between words with other words and sentences prompt in multiple dimensional ways. But word2vec prepares vectors in a unidirectional way, not in a bidirectional way; word2vec fails to find semantic vectors when a word has two meanings, and the meaning depends on adjacent words. Table ​ Table7 7 represents a comparison of Machine Learning models and features extracting methods.

Comparison of models

In AES, cohesion and coherence will check the content of the essay concerning the essay prompt these can be extracted from essay in the vector from. Two more parameters are there to access an essay is completeness and feedback. Completeness will check whether student’s response is sufficient or not though the student wrote correctly. Table ​ Table8 8 represents all four parameters comparison for essay grading. Table ​ Table9 9 illustrates comparison of all approaches based on various features like grammar, spelling, organization of essay, relevance.

Comparison of all models with respect to cohesion, coherence, completeness, feedback

comparison of all approaches on various features

What are the challenges/limitations in the current research?

From our study and results discussed in the previous sections, many researchers worked on automated essay scoring systems with numerous techniques. We have statistical methods, classification methods, and neural network approaches to evaluate the essay automatically. The main goal of the automated essay grading system is to reduce human effort and improve consistency.

The vast majority of essay scoring systems are dealing with the efficiency of the algorithm. But there are many challenges in automated essay grading systems. One should assess the essay by following parameters like the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge.

No model works on the relevance of content, which means whether student response or explanation is relevant to the given prompt or not if it is relevant to how much it is appropriate, and there is no discussion about the cohesion and coherence of the essays. All researches concentrated on extracting the features using some NLP libraries, trained their models, and testing the results. But there is no explanation in the essay evaluation system about consistency and completeness, But Palma and Atkinson ( 2018 ) explained coherence-based essay evaluation. And Zupanc and Bosnic ( 2014 ) also used the word coherence to evaluate essays. And they found consistency with latent semantic analysis (LSA) for finding coherence from essays, but the dictionary meaning of coherence is "The quality of being logical and consistent."

Another limitation is there is no domain knowledge-based evaluation of essays using Machine Learning models. For example, the meaning of a cell is different from biology to physics. Many Machine Learning models extract features with WordVec and GloVec; these NLP libraries cannot convert the words into vectors when they have two or more meanings.

Other challenges that influence the Automated Essay Scoring Systems.

All these approaches worked to improve the QWK score of their models. But QWK will not assess the model in terms of features extraction and constructed irrelevant answers. The QWK is not evaluating models whether the model is correctly assessing the answer or not. There are many challenges concerning students' responses to the Automatic scoring system. Like in evaluating approach, no model has examined how to evaluate the constructed irrelevant and adversarial answers. Especially the black box type of approaches like deep learning models provides more options to the students to bluff the automated scoring systems.

The Machine Learning models that work on statistical features are very vulnerable. Based on Powers et al. ( 2001 ) and Bejar Isaac et al. ( 2014 ), the E-rater was failed on Constructed Irrelevant Responses Strategy (CIRS). From the study of Bejar et al. ( 2013 ), Higgins and Heilman ( 2014 ), observed that when student response contain irrelevant content or shell language concurring to prompt will influence the final score of essays in an automated scoring system.

In deep learning approaches, most of the models automatically read the essay's features, and some methods work on word-based embedding and other character-based embedding features. From the study of Riordan Brain et al. ( 2019 ), The character-based embedding systems do not prioritize spelling correction. However, it is influencing the final score of the essay. From the study of Horbach and Zesch ( 2019 ), Various factors are influencing AES systems. For example, there are data set size, prompt type, answer length, training set, and human scorers for content-based scoring.

Ding et al. ( 2020 ) reviewed that the automated scoring system is vulnerable when a student response contains more words from prompt, like prompt vocabulary repeated in the response. Parekh et al. ( 2020 ) and Kumar et al. ( 2020 ) tested various neural network models of AES by iteratively adding important words, deleting unimportant words, shuffle the words, and repeating sentences in an essay and found that no change in the final score of essays. These neural network models failed to recognize common sense in adversaries' essays and give more options for the students to bluff the automated systems.

Other than NLP and ML techniques for AES. From Wresch ( 1993 ) to Madnani and Cahill ( 2018 ). discussed the complexity of AES systems, standards need to be followed. Like assessment rubrics to test subject knowledge, irrelevant responses, and ethical aspects of an algorithm like measuring the fairness of student response.

Fairness is an essential factor for automated systems. For example, in AES, fairness can be measure in an agreement between human score to machine score. Besides this, From Loukina et al. ( 2019 ), the fairness standards include overall score accuracy, overall score differences, and condition score differences between human and system scores. In addition, scoring different responses in the prospect of constructive relevant and irrelevant will improve fairness.

Madnani et al. ( 2017a ; b ). Discussed the fairness of AES systems for constructed responses and presented RMS open-source tool for detecting biases in the models. With this, one can change fairness standards according to their analysis of fairness.

From Berzak et al.'s ( 2018 ) approach, behavior factors are a significant challenge in automated scoring systems. That helps to find language proficiency, word characteristics (essential words from the text), predict the critical patterns from the text, find related sentences in an essay, and give a more accurate score.

Rupp ( 2018 ), has discussed the designing, evaluating, and deployment methodologies for AES systems. They provided notable characteristics of AES systems for deployment. They are like model performance, evaluation metrics for a model, threshold values, dynamically updated models, and framework.

First, we should check the model performance on different datasets and parameters for operational deployment. Selecting Evaluation metrics for AES models are like QWK, correlation coefficient, or sometimes both. Kelley and Preacher ( 2012 ) have discussed three categories of threshold values: marginal, borderline, and acceptable. The values can be varied based on data size, model performance, type of model (single scoring, multiple scoring models). Once a model is deployed and evaluates millions of responses every time for optimal responses, we need a dynamically updated model based on prompt and data. Finally, framework designing of AES model, hear a framework contains prompts where test-takers can write the responses. One can design two frameworks: a single scoring model for a single methodology and multiple scoring models for multiple concepts. When we deploy multiple scoring models, each prompt could be trained separately, or we can provide generalized models for all prompts with this accuracy may vary, and it is challenging.

Our Systematic literature review on the automated essay grading system first collected 542 papers with selected keywords from various databases. After inclusion and exclusion criteria, we left with 139 articles; on these selected papers, we applied Quality assessment criteria with two reviewers, and finally, we selected 62 writings for final review.

Our observations on automated essay grading systems from 2010 to 2020 are as followed:

• The implementation techniques of automated essay grading systems are classified into four buckets; there are 1. regression models 2. Classification models 3. Neural networks 4. Ontology-based methodology, but using neural networks, the researchers are more accurate than other techniques, and all the methods state of the art provided in Table ​ Table3 3 .
• The majority of the regression and classification models on essay scoring used statistical features to find the final score. It means the systems or models trained on such parameters as word count, sentence count, etc. though the parameters extracted from the essay, the algorithm are not directly training on essays. The algorithms trained on some numbers obtained from the essay and hear if numbers matched the composition will get a good score; otherwise, the rating is less. In these models, the evaluation process is entirely on numbers, irrespective of the essay. So, there is a lot of chance to miss the coherence, relevance of the essay if we train our algorithm on statistical parameters.
• In the neural network approach, the models trained on Bag of Words (BoW) features. The BoW feature is missing the relationship between a word to word and the semantic meaning of the sentence. E.g., Sentence 1: John killed bob. Sentence 2: bob killed John. In these two sentences, the BoW is "John," "killed," "bob."
• In the Word2Vec library, if we are prepared a word vector from an essay in a unidirectional way, the vector will have a dependency with other words and finds the semantic relationship with other words. But if a word has two or more meanings like "Bank loan" and "River Bank," hear bank has two implications, and its adjacent words decide the sentence meaning; in this case, Word2Vec is not finding the real meaning of the word from the sentence.
• The features extracted from essays in the essay scoring system are classified into 3 type's features like statistical features, style-based features, and content-based features, which are explained in RQ2 and Table ​ Table3. 3 . But statistical features, are playing a significant role in some systems and negligible in some systems. In Shehab et al. ( 2016 ); Cummins et al. ( 2016 ). Dong et al. ( 2017 ). Dong and Zhang ( 2016 ). Mathias and Bhattacharyya ( 2018a ; b ) Systems the assessment is entirely on statistical and style-based features they have not retrieved any content-based features. And in other systems that extract content from the essays, the role of statistical features is for only preprocessing essays but not included in the final grading.
• In AES systems, coherence is the main feature to be considered while evaluating essays. The actual meaning of coherence is to stick together. That is the logical connection of sentences (local level coherence) and paragraphs (global level coherence) in a story. Without coherence, all sentences in a paragraph are independent and meaningless. In an Essay, coherence is a significant feature that is explaining everything in a flow and its meaning. It is a powerful feature in AES system to find the semantics of essay. With coherence, one can assess whether all sentences are connected in a flow and all paragraphs are related to justify the prompt. Retrieving the coherence level from an essay is a critical task for all researchers in AES systems.
• In automatic essay grading systems, the assessment of essays concerning content is critical. That will give the actual score for the student. Most of the researches used statistical features like sentence length, word count, number of sentences, etc. But according to collected results, 32% of the systems used content-based features for the essay scoring. Example papers which are on content-based assessment are Taghipour and Ng ( 2016 ); Persing and Ng ( 2013 ); Wang et al. ( 2018a , 2018b ); Zhao et al. ( 2017 ); Kopparapu and De ( 2016 ), Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ); Mohler and Mihalcea ( 2009 ) are used content and statistical-based features. The results are shown in Fig. ​ Fig.3. 3 . And mainly the content-based features extracted with word2vec NLP library, but word2vec is capable of capturing the context of a word in a document, semantic and syntactic similarity, relation with other terms, but word2vec is capable of capturing the context word in a uni-direction either left or right. If a word has multiple meanings, there is a chance of missing the context in the essay. After analyzing all the papers, we found that content-based assessment is a qualitative assessment of essays.
• On the other hand, Horbach and Zesch ( 2019 ); Riordan Brain et al. ( 2019 ); Ding et al. ( 2020 ); Kumar et al. ( 2020 ) proved that neural network models are vulnerable when a student response contains constructed irrelevant, adversarial answers. And a student can easily bluff an automated scoring system by submitting different responses like repeating sentences and repeating prompt words in an essay. From Loukina et al. ( 2019 ), and Madnani et al. ( 2017b ). The fairness of an algorithm is an essential factor to be considered in AES systems.
• While talking about speech assessment, the data set contains audios of duration up to one minute. Feature extraction techniques are entirely different from text assessment, and accuracy varies based on speaking fluency, pitching, male to female voice and boy to adult voice. But the training algorithms are the same for text and speech assessment.
• Once an AES system evaluates essays and short answers accurately in all directions, there is a massive demand for automated systems in the educational and related world. Now AES systems are deployed in GRE, TOEFL exams; other than these, we can deploy AES systems in massive open online courses like Coursera(“ https://coursera.org/learn//machine-learning//exam ”), NPTEL ( https://swayam.gov.in/explorer ), etc. still they are assessing student performance with multiple-choice questions. In another perspective, AES systems can be deployed in information retrieval systems like Quora, stack overflow, etc., to check whether the retrieved response is appropriate to the question or not and can give ranking to the retrieved answers.

Conclusion and future work

As per our Systematic literature review, we studied 62 papers. There exist significant challenges for researchers in implementing automated essay grading systems. Several researchers are working rigorously on building a robust AES system despite its difficulty in solving this problem. All evaluating methods are not evaluated based on coherence, relevance, completeness, feedback, and knowledge-based. And 90% of essay grading systems are used Kaggle ASAP (2012) dataset, which has general essays from students and not required any domain knowledge, so there is a need for domain-specific essay datasets to train and test. Feature extraction is with NLTK, WordVec, and GloVec NLP libraries; these libraries have many limitations while converting a sentence into vector form. Apart from feature extraction and training Machine Learning models, no system is accessing the essay's completeness. No system provides feedback to the student response and not retrieving coherence vectors from the essay—another perspective the constructive irrelevant and adversarial student responses still questioning AES systems.

Our proposed research work will go on the content-based assessment of essays with domain knowledge and find a score for the essays with internal and external consistency. And we will create a new dataset concerning one domain. And another area in which we can improve is the feature extraction techniques.

This study includes only four digital databases for study selection may miss some functional studies on the topic. However, we hope that we covered most of the significant studies as we manually collected some papers published in useful journals.

Below is the link to the electronic supplementary material.

Not Applicable.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Dadi Ramesh, Email: moc.liamg@44hsemaridad .

Suresh Kumar Sanampudi, Email: ni.ca.hutnj@idupmanashserus .

• Adamson, A., Lamb, A., & December, R. M. (2014). Automated Essay Grading.
• Ajay HB, Tillett PI, Page EB (1973) Analysis of essays by computer (AEC-II) (No. 8-0102). Washington, DC: U.S. Department of Health, Education, and Welfare, Office of Education, National Center for Educational Research and Development
• Ajetunmobi SA, Daramola O (2017) Ontology-based information extraction for subject-focussed automatic essay evaluation. In: 2017 International Conference on Computing Networking and Informatics (ICCNI) p 1–6. IEEE
• Alva-Manchego F, et al. (2019) EASSE: Easier Automatic Sentence Simplification Evaluation.” ArXiv abs/1908.04567 (2019): n. pag
• Bailey S, Meurers D (2008) Diagnosing meaning errors in short answers to reading comprehension questions. In: Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications (Columbus), p 107–115
• Basu S, Jacobs C, Vanderwende L. Powergrading: a clustering approach to amplify human effort for short answer grading. Trans Assoc Comput Linguist (TACL) 2013; 1 :391–402. doi: 10.1162/tacl_a_00236. [ CrossRef ] [ Google Scholar ]
• Bejar, I. I., Flor, M., Futagi, Y., & Ramineni, C. (2014). On the vulnerability of automated scoring to construct-irrelevant response strategies (CIRS): An illustration. Assessing Writing, 22, 48-59.
• Bejar I, et al. (2013) Length of Textual Response as a Construct-Irrelevant Response Strategy: The Case of Shell Language. Research Report. ETS RR-13-07.” ETS Research Report Series (2013): n. pag
• Berzak Y, et al. (2018) “Assessing Language Proficiency from Eye Movements in Reading.” ArXiv abs/1804.07329 (2018): n. pag
• Blanchard D, Tetreault J, Higgins D, Cahill A, Chodorow M (2013) TOEFL11: A corpus of non-native English. ETS Research Report Series, 2013(2):i–15, 2013
• Blood, I. (2011). Automated essay scoring: a literature review. Studies in Applied Linguistics and TESOL, 11(2).
• Burrows S, Gurevych I, Stein B. The eras and trends of automatic short answer grading. Int J Artif Intell Educ. 2015; 25 :60–117. doi: 10.1007/s40593-014-0026-8. [ CrossRef ] [ Google Scholar ]
• Cader, A. (2020, July). The Potential for the Use of Deep Neural Networks in e-Learning Student Evaluation with New Data Augmentation Method. In International Conference on Artificial Intelligence in Education (pp. 37–42). Springer, Cham.
• Cai C (2019) Automatic essay scoring with recurrent neural network. In: Proceedings of the 3rd International Conference on High Performance Compilation, Computing and Communications (2019): n. pag.
• Chen M, Li X (2018) "Relevance-Based Automated Essay Scoring via Hierarchical Recurrent Model. In: 2018 International Conference on Asian Language Processing (IALP), Bandung, Indonesia, 2018, p 378–383, doi: 10.1109/IALP.2018.8629256
• Chen Z, Zhou Y (2019) "Research on Automatic Essay Scoring of Composition Based on CNN and OR. In: 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, p 13–18, doi: 10.1109/ICAIBD.2019.8837007
• Contreras JO, Hilles SM, Abubakar ZB (2018) Automated essay scoring with ontology based on text mining and NLTK tools. In: 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 1-6
• Correnti R, Matsumura LC, Hamilton L, Wang E. Assessing students’ skills at writing analytically in response to texts. Elem Sch J. 2013; 114 (2):142–177. doi: 10.1086/671936. [ CrossRef ] [ Google Scholar ]
• Cummins, R., Zhang, M., & Briscoe, E. (2016, August). Constrained multi-task learning for automated essay scoring. Association for Computational Linguistics.
• Darwish SM, Mohamed SK (2020) Automated essay evaluation based on fusion of fuzzy ontology and latent semantic analysis. In: Hassanien A, Azar A, Gaber T, Bhatnagar RF, Tolba M (eds) The International Conference on Advanced Machine Learning Technologies and Applications
• Dasgupta T, Naskar A, Dey L, Saha R (2018) Augmenting textual qualitative features in deep convolution recurrent neural network for automatic essay scoring. In: Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications p 93–102
• Ding Y, et al. (2020) "Don’t take “nswvtnvakgxpm” for an answer–The surprising vulnerability of automatic content scoring systems to adversarial input." In: Proceedings of the 28th International Conference on Computational Linguistics
• Dong F, Zhang Y (2016) Automatic features for essay scoring–an empirical study. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing p 1072–1077
• Dong F, Zhang Y, Yang J (2017) Attention-based recurrent convolutional neural network for automatic essay scoring. In: Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) p 153–162
• Dzikovska M, Nielsen R, Brew C, Leacock C, Gi ampiccolo D, Bentivogli L, Clark P, Dagan I, Dang HT (2013a) Semeval-2013 task 7: The joint student response analysis and 8th recognizing textual entailment challenge
• Dzikovska MO, Nielsen R, Brew C, Leacock C, Giampiccolo D, Bentivogli L, Clark P, Dagan I, Trang Dang H (2013b) SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge. *SEM 2013: The First Joint Conference on Lexical and Computational Semantics
• Educational Testing Service (2008) CriterionSM online writing evaluation service. Retrieved from http://www.ets.org/s/criterion/pdf/9286_CriterionBrochure.pdf .
• Evanini, K., & Wang, X. (2013, August). Automated speech scoring for non-native middle school students with multiple task types. In INTERSPEECH (pp. 2435–2439).
• Foltz PW, Laham D, Landauer TK (1999) The Intelligent Essay Assessor: Applications to Educational Technology. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 1, 2, http://imej.wfu.edu/articles/1999/2/04/ index.asp
• Granger, S., Dagneaux, E., Meunier, F., & Paquot, M. (Eds.). (2009). International corpus of learner English. Louvain-la-Neuve: Presses universitaires de Louvain.
• Higgins D, Heilman M. Managing what we can measure: quantifying the susceptibility of automated scoring systems to gaming behavior” Educ Meas Issues Pract. 2014; 33 :36–46. doi: 10.1111/emip.12036. [ CrossRef ] [ Google Scholar ]
• Horbach A, Zesch T. The influence of variance in learner answers on automatic content scoring. Front Educ. 2019; 4 :28. doi: 10.3389/feduc.2019.00028. [ CrossRef ] [ Google Scholar ]
• https://www.coursera.org/learn/machine-learning/exam/7pytE/linear-regression-with-multiple-variables/attempt
• Hussein, M. A., Hassan, H., & Nassef, M. (2019). Automated language essay scoring systems: A literature review. PeerJ Computer Science, 5, e208. [ PMC free article ] [ PubMed ]
• Ke Z, Ng V (2019) “Automated essay scoring: a survey of the state of the art.” IJCAI
• Ke, Z., Inamdar, H., Lin, H., & Ng, V. (2019, July). Give me more feedback II: Annotating thesis strength and related attributes in student essays. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3994-4004).
• Kelley K, Preacher KJ. On effect size. Psychol Methods. 2012; 17 (2):137–152. doi: 10.1037/a0028086. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kitchenham B, Brereton OP, Budgen D, Turner M, Bailey J, Linkman S. Systematic literature reviews in software engineering–a systematic literature review. Inf Softw Technol. 2009; 51 (1):7–15. doi: 10.1016/j.infsof.2008.09.009. [ CrossRef ] [ Google Scholar ]
• Klebanov, B. B., & Madnani, N. (2020, July). Automated evaluation of writing–50 years and counting. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 7796–7810).
• Knill K, Gales M, Kyriakopoulos K, et al. (4 more authors) (2018) Impact of ASR performance on free speaking language assessment. In: Interspeech 2018.02–06 Sep 2018, Hyderabad, India. International Speech Communication Association (ISCA)
• Kopparapu SK, De A (2016) Automatic ranking of essays using structural and semantic features. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), p 519–523
• Kumar, Y., Aggarwal, S., Mahata, D., Shah, R. R., Kumaraguru, P., & Zimmermann, R. (2019, July). Get it scored using autosas—an automated system for scoring short answers. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 9662–9669).
• Kumar Y, et al. (2020) “Calling out bluff: attacking the robustness of automatic scoring systems with simple adversarial testing.” ArXiv abs/2007.06796
• Li X, Chen M, Nie J, Liu Z, Feng Z, Cai Y (2018) Coherence-Based Automated Essay Scoring Using Self-attention. In: Sun M, Liu T, Wang X, Liu Z, Liu Y (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL 2018, NLP-NABD 2018. Lecture Notes in Computer Science, vol 11221. Springer, Cham. 10.1007/978-3-030-01716-3_32
• Liang G, On B, Jeong D, Kim H, Choi G. Automated essay scoring: a siamese bidirectional LSTM neural network architecture. Symmetry. 2018; 10 :682. doi: 10.3390/sym10120682. [ CrossRef ] [ Google Scholar ]
• Liua, H., Yeb, Y., & Wu, M. (2018, April). Ensemble Learning on Scoring Student Essay. In 2018 International Conference on Management and Education, Humanities and Social Sciences (MEHSS 2018). Atlantis Press.
• Liu J, Xu Y, Zhao L (2019) Automated Essay Scoring based on Two-Stage Learning. ArXiv, abs/1901.07744
• Loukina A, et al. (2015) Feature selection for automated speech scoring.” BEA@NAACL-HLT
• Loukina A, et al. (2017) “Speech- and Text-driven Features for Automated Scoring of English-Speaking Tasks.” SCNLP@EMNLP 2017
• Loukina A, et al. (2019) The many dimensions of algorithmic fairness in educational applications. BEA@ACL
• Lun J, Zhu J, Tang Y, Yang M (2020) Multiple data augmentation strategies for improving performance on automatic short answer scoring. In: Proceedings of the AAAI Conference on Artificial Intelligence, 34(09): 13389-13396
• Madnani, N., & Cahill, A. (2018, August). Automated scoring: Beyond natural language processing. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 1099–1109).
• Madnani N, et al. (2017b) “Building better open-source tools to support fairness in automated scoring.” EthNLP@EACL
• Malinin A, et al. (2016) “Off-topic response detection for spontaneous spoken english assessment.” ACL
• Malinin A, et al. (2017) “Incorporating uncertainty into deep learning for spoken language assessment.” ACL
• Mathias S, Bhattacharyya P (2018a) Thank “Goodness”! A Way to Measure Style in Student Essays. In: Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications p 35–41
• Mathias S, Bhattacharyya P (2018b) ASAP++: Enriching the ASAP automated essay grading dataset with essay attribute scores. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
• Mikolov T, et al. (2013) “Efficient Estimation of Word Representations in Vector Space.” ICLR
• Mohler M, Mihalcea R (2009) Text-to-text semantic similarity for automatic short answer grading. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009) p 567–575
• Mohler M, Bunescu R, Mihalcea R (2011) Learning to grade short answer questions using semantic similarity measures and dependency graph alignments. In: Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies p 752–762
• Muangkammuen P, Fukumoto F (2020) Multi-task Learning for Automated Essay Scoring with Sentiment Analysis. In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop p 116–123
• Nguyen, H., & Dery, L. (2016). Neural networks for automated essay grading. CS224d Stanford Reports, 1–11.
• Palma D, Atkinson J. Coherence-based automatic essay assessment. IEEE Intell Syst. 2018; 33 (5):26–36. doi: 10.1109/MIS.2018.2877278. [ CrossRef ] [ Google Scholar ]
• Parekh S, et al (2020) My Teacher Thinks the World Is Flat! Interpreting Automatic Essay Scoring Mechanism.” ArXiv abs/2012.13872 (2020): n. pag
• Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532–1543).
• Persing I, Ng V (2013) Modeling thesis clarity in student essays. In:Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) p 260–269
• Powers DE, Burstein JC, Chodorow M, Fowles ME, Kukich K. Stumping E-Rater: challenging the validity of automated essay scoring. ETS Res Rep Ser. 2001; 2001 (1):i–44. [ Google Scholar ]
• Powers DE, Burstein JC, Chodorow M, Fowles ME, Kukich K. Stumping e-rater: challenging the validity of automated essay scoring. Comput Hum Behav. 2002; 18 (2):103–134. doi: 10.1016/S0747-5632(01)00052-8. [ CrossRef ] [ Google Scholar ]
• Ramachandran L, Cheng J, Foltz P (2015) Identifying patterns for short answer scoring using graph-based lexico-semantic text matching. In: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications p 97–106
• Ramanarayanan V, et al. (2017) “Human and Automated Scoring of Fluency, Pronunciation and Intonation During Human-Machine Spoken Dialog Interactions.” INTERSPEECH
• Riordan B, Horbach A, Cahill A, Zesch T, Lee C (2017) Investigating neural architectures for short answer scoring. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications p 159–168
• Riordan B, Flor M, Pugh R (2019) "How to account for misspellings: Quantifying the benefit of character representations in neural content scoring models."In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
• Rodriguez P, Jafari A, Ormerod CM (2019) Language models and Automated Essay Scoring. ArXiv, abs/1909.09482
• Rudner, L. M., & Liang, T. (2002). Automated essay scoring using Bayes' theorem. The Journal of Technology, Learning and Assessment, 1(2).
• Rudner, L. M., Garcia, V., & Welch, C. (2006). An evaluation of IntelliMetric™ essay scoring system. The Journal of Technology, Learning and Assessment, 4(4).
• Rupp A. Designing, evaluating, and deploying automated scoring systems with validity in mind: methodological design decisions. Appl Meas Educ. 2018; 31 :191–214. doi: 10.1080/08957347.2018.1464448. [ CrossRef ] [ Google Scholar ]
• Ruseti S, Dascalu M, Johnson AM, McNamara DS, Balyan R, McCarthy KS, Trausan-Matu S (2018) Scoring summaries using recurrent neural networks. In: International Conference on Intelligent Tutoring Systems p 191–201. Springer, Cham
• Sakaguchi K, Heilman M, Madnani N (2015) Effective feature integration for automated short answer scoring. In: Proceedings of the 2015 conference of the North American Chapter of the association for computational linguistics: Human language technologies p 1049–1054
• Salim, Y., Stevanus, V., Barlian, E., Sari, A. C., & Suhartono, D. (2019, December). Automated English Digital Essay Grader Using Machine Learning. In 2019 IEEE International Conference on Engineering, Technology and Education (TALE) (pp. 1–6). IEEE.
• Shehab A, Elhoseny M, Hassanien AE (2016) A hybrid scheme for Automated Essay Grading based on LVQ and NLP techniques. In: 12th International Computer Engineering Conference (ICENCO), Cairo, 2016, p 65-70
• Shermis MD, Mzumara HR, Olson J, Harrington S. On-line grading of student essays: PEG goes on the World Wide Web. Assess Eval High Educ. 2001; 26 (3):247–259. doi: 10.1080/02602930120052404. [ CrossRef ] [ Google Scholar ]
• Stab C, Gurevych I (2014) Identifying argumentative discourse structures in persuasive essays. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) p 46–56
• Sultan MA, Salazar C, Sumner T (2016) Fast and easy short answer grading with high accuracy. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies p 1070–1075
• Süzen, N., Gorban, A. N., Levesley, J., & Mirkes, E. M. (2020). Automatic short answer grading and feedback using text mining methods. Procedia Computer Science, 169, 726–743.
• Taghipour K, Ng HT (2016) A neural approach to automated essay scoring. In: Proceedings of the 2016 conference on empirical methods in natural language processing p 1882–1891
• Tashu TM (2020) "Off-Topic Essay Detection Using C-BGRU Siamese. In: 2020 IEEE 14th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, p 221–225, doi: 10.1109/ICSC.2020.00046
• Tashu TM, Horváth T (2019) A layered approach to automatic essay evaluation using word-embedding. In: McLaren B, Reilly R, Zvacek S, Uhomoibhi J (eds) Computer Supported Education. CSEDU 2018. Communications in Computer and Information Science, vol 1022. Springer, Cham
• Tashu TM, Horváth T (2020) Semantic-Based Feedback Recommendation for Automatic Essay Evaluation. In: Bi Y, Bhatia R, Kapoor S (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham
• Uto M, Okano M (2020) Robust Neural Automated Essay Scoring Using Item Response Theory. In: Bittencourt I, Cukurova M, Muldner K, Luckin R, Millán E (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham
• Wang Z, Liu J, Dong R (2018a) Intelligent Auto-grading System. In: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) p 430–435. IEEE.
• Wang Y, et al. (2018b) “Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning.” EMNLP
• Zhu W, Sun Y (2020) Automated essay scoring system using multi-model Machine Learning, david c. wyld et al. (eds): mlnlp, bdiot, itccma, csity, dtmn, aifz, sigpro
• Wresch W. The Imminence of Grading Essays by Computer-25 Years Later. Comput Compos. 1993; 10 :45–58. doi: 10.1016/S8755-4615(05)80058-1. [ CrossRef ] [ Google Scholar ]
• Wu, X., Knill, K., Gales, M., & Malinin, A. (2020). Ensemble approaches for uncertainty in spoken language assessment.
• Xia L, Liu J, Zhang Z (2019) Automatic Essay Scoring Model Based on Two-Layer Bi-directional Long-Short Term Memory Network. In: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence p 133–137
• Yannakoudakis H, Briscoe T, Medlock B (2011) A new dataset and method for automatically grading ESOL texts. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies p 180–189
• Zhao S, Zhang Y, Xiong X, Botelho A, Heffernan N (2017) A memory-augmented neural model for automated grading. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale p 189–192
• Zupanc K, Bosnic Z (2014) Automated essay evaluation augmented with semantic coherence measures. In: 2014 IEEE International Conference on Data Mining p 1133–1138. IEEE.
• Zupanc K, Savić M, Bosnić Z, Ivanović M (2017) Evaluating coherence of essays using sentence-similarity networks. In: Proceedings of the 18th International Conference on Computer Systems and Technologies p 65–72
• Dzikovska, M. O., Nielsen, R., & Brew, C. (2012, June). Towards effective tutorial feedback for explanation questions: A dataset and baselines. In  Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  (pp. 200-210).
• Kumar, N., & Dey, L. (2013, November). Automatic Quality Assessment of documents with application to essay grading. In 2013 12th Mexican International Conference on Artificial Intelligence (pp. 216–222). IEEE.
• Wu, S. H., & Shih, W. F. (2018, July). A short answer grading system in chinese by support vector approach. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications (pp. 125-129).
• Agung Putri Ratna, A., Lalita Luhurkinanti, D., Ibrahim I., Husna D., Dewi Purnamasari P. (2018). Automatic Essay Grading System for Japanese Language Examination Using Winnowing Algorithm, 2018 International Seminar on Application for Technology of Information and Communication, 2018, pp. 565–569. 10.1109/ISEMANTIC.2018.8549789.
• Sharma A., & Jayagopi D. B. (2018). Automated Grading of Handwritten Essays 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018, pp 279–284. 10.1109/ICFHR-2018.2018.00056

Articles, advice and tutorials from the Essay team

Writing Philosophy

How to write with Essay

Advanced Features

How to use the advanced features of Essay

Tutorial Articles

A collection of articles to help a writer follow Dr. Jordan B Peterson's essay writing process using essay.app

Fast Essay Writing Help

Get help with your paper in 3 hours, how it works, what people are saying.

“I never thought I would say something like this, but getting finished orders back from Ivy Panda always greatly improves my mood. Getting something done study-wise improves mood....”

“Like I thought, Ivy Panda provides highly professional help. besides individual orders, you can get lots of excellent tips and advice on their study blog. This site is a real...”

“I've been using this website for almost two years, and I'm still surprised by the quality of their papers. I definitely recommend it to everyone!”

“Loving the fat that they have a lot of experts qualified in different academic areas.”

“I can't imagine handling studies without Ivypanda. They have everything in order and are never late. This is way my student life is way more organized than before.”

“I like how easy it is to use Ivypanda's website. It took me five minutes to create an account and get the help I needed.”

“I never realized how convenient and affordable this service would be. Placing an order is effortless, and I can forget about it for days. It's the ideal solution for students in...”

“Of course, I used to write essays at school, but when I got my first 2000-word written assignment at college, I was shocked. What could you possibly write there? Luckily, my...”

“Whenever I need to write something for college, it takes me forever to create an interesting hook. But now I'm a constant user of Ivypanda's database. The thing that I like most...”

“I have no complaints regarding the quality of the paper. However, it took me years to send a request. I think they should fix this problem asap.”

“If you're like me and spend twice as much time on tasks that could be done faster, I recommend you to try out this amazing website. I used it as a last resort, but quickly realized...”

“If half a year ago I had been told that all my study problems would be solved in 2 weeks, I would never believe. It's a miracle! I fixed all my grades!”

“My endless respect to the team of Ivypanda! Each worker is very professional, to say the least. And I really enjoyed working with the expert Anna. I hope to do it again.”

“During busy periods, I strive for maximum productivity, and ivy panda plays a significant role in helping me achieve that. I'm immensely grateful for all the assistance they...”

“I'm super glad I found this website, as I was starting to get fed up with other services I use. Yes, multiple services, as none of them could be trusted. Sometimes I would even...”

“I do not use services often, but Ivy Panda literally saved my life last week! I totally forgot about the deadline, but the experts managed to do everything on time.”

“I like that after placing an order, you get to talk to the expert individually. It is very productive to discuss complex issues with a professional. Thank you for such an...”

“It feels that Ivypanda becomes more and more expensive for me. Not every student can afford such prices, and sometimes I don't even get what I wanted for 100%.”

“I like writing, but it's not always easy to develop a reasonable topic that is not over-researched. Ivypanda helps me a lot with this issue because it's full of fresh papers on...”

“I major in PE, and I tend to struggle with language-related subjects. This website always helps me catch up with the composition and academic writing courses.”

Thousands of students improved their grades with us

You’ll also get, our customers attend.

How can you help me with my assignment?

At IvyPanda we help you reach your academic goals by pairing you with academic experts in your field of study to help with assignments. Our experts go above and beyond to provide the best support, in line with our Fair Use Policy. With our extensive cohort of experts, we can ensure fast access to the help you need.

Who are your academic experts?

We source the best talent globally. Our experts are based in the USA, UK, Australia and in many other countries. We ensure that all our experts hold a minimum of a Master’s degree, with many possessing a Ph.D. in their area of expertise. Our recruitment process is rigorous with only 2 out of every 100 candidates getting accepted into our expert team.

Is it safe to use IvyPanda?

Privacy is paramount. We take extensive security measures to ensure that all data transmissions are encrypted and secure. We use SSL encryption, do not share any information with third parties and require that not even the academic experts you work with have access to identifying personal information. We use TrustedSite® and other vendors for maximum site security.

Do you use any AI (artificial intelligence) tools?

No. All orders at IvyPanda are handled by experienced academic experts. Additionally, we check every work for plagiarism and potential AI use with the help of specialized software based on the same algorithms as ChatGPT, GoCharlie, and other similar AI tools. As a result, you get entirely original work that's also 100% tailored to your instructions.

Should I pay in advance?

Placing an order with us requires immediate payment. However, it is possible to make an inquiry to ensure we can undertake your request before paying. In addition, we offer a full 100% money-back guarantee on all orders.

What if I don't like the delivered order?

We pride ourselves on offering a quality service. If you are unhappy with a completed order or would like something changed, we offer unlimited revisions for 14 days. Providing us with detailed feedback about the desired changes helps us address the issue fast. Our experts correct any and all mistakes pertaining to the initial service request. In addition, our Superior services have an extended revision period of 45 days, and our 1st Class services have a revision period of 90 days. We also offer a 100% money-back guarantee on all orders.

What are the benefits of the 1st Class and Superior plans?

1st Class and Superior orders are undertaken by our top-rated experts to ensure an exceptional quality of work. Your orders and requests are given priority status and you are provided with VIP support. In addition to your completed order, you also receive a quality report to confirm that your order has been verified and undergone multiple checks to ensure it meets your specifications. Superior services have an extended revision period of 45 days, and our 1st Class services have a revision period of 90 days.

Ask our support team

Professional Essay Writing & Editing Services

Our experts will write you a top-quality paper and revise it an unlimited number of times until you're 100% satisfied - or offer a refund.

Will my paper pass Turnitin?

Yes, definitely. Our experts write all papers from the ground up and use only credible sources. They also are familiar with all formatting and referencing styles.

Once your paper is completed, it is checked for plagiarism with powerful plagiarism detection software (e.g., Copyscape and Copyleaks) to make sure it is 100% original and will pass any checker, including Turnitin.

We do not check completed papers with Turnitin because it saves all works to the database, making them unoriginal if submitted once again.

If you'd like to get official proof of your paper's uniqueness, add our Plagiarism Report extra to your order.

Can somebody detect that I've used an essay writing service?

Absolutely not. GradeSmiths is a reliable essay writing service that takes all measures to protect your data and order details from illegal access or disclosure. We work in accordance with GDPR, use the latest bank-level encryption, and offer anonymous communication with writers. So, it's 100% safe to order your papers from our subject-matter experts.

What if I don't like the quality of my paper?

No problem. In such cases, which are quite rare, we offer two options to choose from: you can either request a free revision or get a refund.

If you would like the writer to amend the final draft, just activate the 14-day revision period in your account and add revision instructions. Then, the writer will revise the paper according to your remarks in the shortest time possible.

To request a refund, contact the Support Team via email or live chat. They are always more than happy to help out. Please note that this option is available within 14-30 days, depending on the paper’s length.

I'm not sure that your writers can deal with my topic?

We have a vast pool of qualified experts across 50 subject areas. All of them have 4+ years of academic writing experience, so rest assured they will be able to deal with your topic.

Still hesitating? Please contact our 24/7 support team to discuss your project in more detail and get a free quote.

Do you deal with large papers like research papers, dissertations, etc.?

Yes, we have experts in huge papers of any complexity, including:

• research paper
• capstone project
• dissertation

To see the complete list of papers we can prepare for you, click here .

Do you write all types of papers?

Yes. We can help during the entire study period and post-study life — from preparing admission essays to assisting with graduation papers and resumes. Also, our services are not limited to writing. We provide editing, proofreading, and paraphrasing services too.

If you haven't found your type of work on the order page, just select "Other" and specify what exactly you need to be done. Then, we'll check your paper details and find an appropriate expert for you.

Is your writing service legal?

Yes, of course. GradeSmiths is a registered company with a 12-year history of delivering academic assistance services to English-speaking students worldwide that works following existing laws. Our main goal is to help students improve their writing skills and academic performance.

Over 50k students trust us, 42% of whom come by friend's recommendation.

How fast can your service complete an essay?

Our experts can write a high-quality 275-word or 1-page essay for you within just 1 hour. However, if your essay is 550+ words long or 2+ pages, it will take them 3 hours to complete it to a high standard.

What happens after I pay?

Once the payment is done, you get access to the personal customer area, where you can track order progress, add more instructions for the writer or ask them order-related questions.

In your customer area, you can also communicate with the support team, request revisions, add extra services like additional pages or excel sheets to your order, rate your writer, and much more.

Can I add additional instructions after the order has been placed?

Yes, sure. You can do it on the order page in your customer area. The writer will check them out and follow too.

Want to talk to my writer. Can I?

Yes, definitely. You will be able to communicate with the writer through messages in your account after the payment is done and the writer is assigned. Usually, it takes up to 15 minutes.

Please note you will receive an email notification once the writer is assigned.

To send a message to your writer, go to the corresponding order page and type your message in the chat window located in the upper right-hand corner of the page.

How much does paper writing help cost?

The price for writing your paper greatly depends on your study level, type of work, deadline, and number of pages needed. For example, a college essay due in 7 days will cost you $7.00 per page, while a Ph.D. dissertation due in 30 days will cost $6.19 per page.

If you want to get the best price, simply place your order in advance. Close deadlines require more resources. Therefore, they are more expensive.

To calculate the price of your order, click here .

What free options do you have?

At GradeSmiths, every paper comes with the following freebies:

• Bibliography page
• Any academic format
• Preferred paper writer
• Limitless revision
• Order tracking option
• Quality check
• 24/7 customer support

Do you have discounts?

Our professional aid is accessible to everyone. We strive to help students on any budget. For this purpose, we offer regular discounts and seasonal deals to all customers. In addition, every newcomer gets a special 15% discount on the first order, while regular customers receive loyalty bonuses they can spend on future orders.

How can I pay?

You can pay for your order using a debit or credit card by Visa, MasterCard, or Maestro. The payment will be processed via a secure checkout page, and no card data will be saved.

Meet Your Perfect Essay Writing Service – GradeSmiths

GradeSmiths is an essay writing service that helps students in the USA and from all over the world to write better essays and other types of high school, college, and university research papers. Our writers are experienced and competent enough to cover all subjects and meet any deadlines.

GradeSmiths’s essay writing service is perfect for students who want to improve their GPA, not wasting too much time and money. Those who’ve tried professional essay writing services once confirm that living up with the workload becomes easier. So, if you have the money to spend on a quality custom essay, GradeSmiths is the perfect writing website for you.

– How much does it cost me to write an essay? We know that this question is important for you. That’s why we guarantee the best price to write an essay brought to you by the Essay Writer service. Our prices start from only $10.52 per page, including the extras. If you’re on a budget, then our essay service is exactly what you need to ace your task for an affordable price.

Team up with the essay writing industry’s finest writers and fulfill your academic goals easier as if you’ve been doing all the homework on your own. Also, get better grades and enjoy your student life to the maximum with GradeSmiths!

7 Easy Signs You Need an Expert Essay Writer Right Now

The ultimate trick to score the highest grade for an essay is… not to write it yourself. Trust writing an essay to an expert essay writer and get this burden off your back. Using a professional to writing essays and our extensive academic knowledge base, we guarantee to fulfill your term paper, case study, book report, or any other type of paper up to the hilt.

We’ll write you a quality essay based on all the order requirements that you initially provided on the order now page. This will save your free time and also boost your GPA. Need more proof? Here are seven signals you might need professional writing help with essays ASAP:

• Essay writing isn’t your strongest perk, so you might want to seek services from professional essay writers online.
• As a student, you lack time and talent to carry out original topic research. Not everyone is made a qualified scholarly researcher. If that’s an issue, we’ll be glad to help.
• You are bad at essay writing. Not every student can master the art of academic essay writing. There is nothing to be ashamed of in case expressing your thoughts and ideas using a proper academic English language is a challenging task.
• There are some of the homework instructions that you don’t understand. Such assignments often occur, puzzling students a lot and making them look for someone to help them with these mind-wrecking tasks.
• There’s no time to do an essay yourself. Things happen in life that you can’t control or predict. Everyone’s a human being who deserves a little bit of you-time now and then. To manage your time more effectively, you can use our essay service.
• Having more free time is great, but, at the same time, risking your academic record isn’t an option. That said, specialists of our service will cover up for you at any time.
• The finals weeks are too stressful, leaving you little to no time to cope with homework assignments.

Get rid of nasty academic issues – hire an academic essay writer with confidence and kiss academic problems goodbye! Our service will meet all your needs and save a good sum of money.

No.1 Essay Writer Website in 2022

Are you facing trouble with various college assignments? Nights without sleep, too difficult topics, reluctant teachers, no one available to help you? Sounds like it’s high time for you to use our essay service. With the help of one of our cheap and competent writers, you could put the finger on any task within just a few hours.

To any college writing problem, there’s a sound solution to be found. Which is, as far as our service is concerned, hiring a skilled essay writer online is the best way out. The great news is that with the help of our competent essay service, all your writing hardships disappear for good. Our expert essay writers, qualified editors, and smart proofreaders work day and night to help you to fulfill your college goals. Choose any of the 35+ subjects that we cover, 50+ types of assignments, and any urgency, including our super-quick 1-hour deadline, to ensure a positive and rapid GPA growth. Order professional essay writing help on GradeSmiths today!

Text us “write my essay for me” – we will take care of the project and improve your academic record. Please, ask any emerging questions. Support managers operate 24/7 to provide you with timely answers and help you with any essay writing emergency. Our service is available all day and night if you have any essay writing emergencies that need urgent academic treatment.

Essay Writing Service You Can Trust

GradeSmiths is not only a cheap essay writing company to get ace-quality writing services, but also a reliable writing service that takes full care of its customers. To protect your data and confidentiality, we take the following measures:

• We keep your data safe.

Your privacy is under careful protection. Cutting-edge encryption solutions protect data on our safe essay writer website. Except for critical essay topics, basic instructions, and contact email, customers don’t have to provide their private information. Plus, you can think of any preferred name and communicate with us as incognito.

• We provide you with secure payment options.

Financial operations are also well-guarded. Our essay writing company cooperates with leading financial service providers, acknowledged all across the world. Use any safe option to purchase your article online.

• We give a chargeless review period.

Viewpoints differ. To avoid dissatisfaction with our essay services, every order is supported by a free review period. It starts on the day of delivery and lasts for 2 weeks. If your essay needs changes, do not hesitate to reach out to us.

• We return your payment if you dislike the essay.

Viewpoints can differ dramatically. The situation is highly surreal, but as a safeguard, your rights for repayment are protected by our Refund policy. Proven plagiarisms, poor quality, and insufficient research are among the main reasons to request reimbursement.

We Have Pro Essay Writers

Let’s skip generalities and get down to our biggest pride – a team of expert essay writers. Nothing proves our expertise and reliability better than the following facts about them:

• Academic credentials: all our essay writers hold advanced university degrees.
• Professional background: we hire essay writers with a proven track record and at least a few years of operation in custom writing.
• Excellent command of English: every paper writer is a native English speaker and an accredited certificate that proves his proficiency in the language.
• Any academic level: pro essay writers complete orders for college, undergraduate, Master’s, and Ph.D. students.
• Any type of assignment: whatever your issue is, we cover 50+ types of various academic tasks.

On top of that, editors and proofreaders help students with their academic papers. They double-check every essay for conformity with directives, originality & quality adherence, formatting standards. In case your essay writer has made a mistake, an assigned editor will see and fix it. In the end, you are assured of receiving a 100% mistake-free and unique essay.

Message us “write my essay” now, and we will select the best-matching essay writer for your demand among the leading college experts.

How Our Essay Writers Service Works

On GradeSmiths, we have a rule that we never break – write only plagiarism-free essays. Every essay writer has to write from scratch. Otherwise, they won’t be working for our service.

All college essay writers you’ll be working with when making orders on our website are real human experts, each across their subject field. Some writers are experienced and educated enough to write essays in several disciplines. We neither rewrite old essays nor take stuff from the internet, simply changing a title and switching paragraphs’ places. Quite the opposite!

Our professional paper writers deliver 100% original scholarly documents, e.g., essays, which live up to the initial order instructions that you provided when making an order. This way, it’s safe to say that our essays are one-of-a-kind – no two similar papers could ever be found if written by our authors.

In general, when writing essays, our writers stick to the following protocol:

• Read writing instructions.
• College relevant background sources.
• Find quotes and reference data.
• Create an essay outline.
• Write a thesis.
• Write the first draft.
• Edit and proofread.
• Apply a required citation style according to the latest APA/MLA/Chicago/Turabian guidelines.
• Check an essay for plagiarism.
• Revise the final draft.

Note that free revisions (14-30 days) are already included in the price. GradeSmiths’s online essay writing service is designed in a way to leave you nothing but to enjoy the rest of your day while one of the service’s writers takes care of your homework.

GradeSmiths’s essay service makes college life easier. Simply make an order now and see for yourself!

Our Guarantees

Why is our essay service so special? Besides having an excellent reputation of reliable essay writers and a service that has low prices, it’s possible to name 5 more features that make GradeSmiths the number one spot for students who want a quality essay for a reasonable price. To learn more advantages that might interest you, please contact the support manager.

In general, our essay writing service provides the following guarantees:

• Originality. Are you worried about whether your essay will be original? We pay great attention to the academic integrity of our customers. That’s why, rest assured, there’s no place for plagiarism on GradeSmiths. Our essays undergo originality verification with advanced plagiarism detection software. If necessary, we can support your composition with a detailed plagiarism report.
• Quality. Sure, you are worried that the requested content might come of poor quality. To prevent this from happening, we let only professional essay writers work on the orders. Candidates for essay writing positions undergo a 4-level selection procedure to verify their expertise in the required discipline field and writing skills.
• Verification. People make mistakes. Our professional college essay writers are not an exception. That is why we double-check completed papers for mistakes and logical flaws. Acknowledged editors go through the text, fixing grammatical and formatting errors. With their final touch, your university essay transforms from an uncut stone to a polished diamond.
• Timeliness. Essay writing is a long and stressful process for students – but not for professional authors. Their expertise allows finishing all types of college papers promptly and by the requested target date. If you need your essay in a couple of hours, you will most certainly have it!

Our FREE Essay Writing Services

Everyone can use essay writing help from GradeSmiths anytime. We strive to help students who have shoestring budgets. For this purpose, our essay service offers regular discounts and juicy deals. Every newcomer gets a special 15% discount for the first order and is offered to join the Customers Loyalty program to receive and collect bonuses for further orders. On top of that, we complement every essay with the following freebies:

• Title page.
• Bibliography page.
• APA/MLA/Chicago/Turabian citation format.
• Best available essay writer.
• Limitless amendments.
• Order tracking option.
• Quality control.
• 24/7 client center.

If you need professional college paper writers who can do the work for cheap, simply submit your order in advance. Close deadlines require more resources. Therefore, they are slightly more expensive. Recruit your top essay writer beforehand, and take advantage of our professional services at a reduced cost.

Write My Essay Is Easy With GradeSmiths

GradeSmiths is the perfect write my essay service. There are professional GradeSmiths, top-notch 24/7 customer support service, cheap prices, a money-back guarantee, and results that you notice right after the first order. The “who can write my essay for me?” question won’t bother you again. Sleepless nights and poor grades will be in the past when you order your first essay on our website.

– Can you write my essay cheap? No problem, GradeSmiths will be glad to write your essay cheap, keeping the quality of writing high, as usual. Use your 15% Welcome discount or spend bonus funds earned from making previous orders to pay for your essay cheap.

– I need a good service to write my essay online. Consider you’ve just found it! GradeSmiths is one of the best services for writing essays in 2022. It’s a legit company that knows what its customers want. You can always count on exceptional customer care, writers who don’t charge an arm and a leg, and timely delivery of your order. Simply order an essay on GradeSmiths and download it when it’s done.

– I want a professional essay writer to write my essay. GradeSmiths doesn’t have writers who don’t know how to write essays. On the contrary, essay writers that we have provide the best service possible thanks to their educational background, academic writing talent, and years of essay writing experience .

Are you looking for a service that can write you a 100% original essay cheap? GradeSmiths is a writer service that gets the job done to a T. Choose the essay writing service that you need, and let’s do this!

• RESEARCH PAPER FOR SALE
• RESEARCH PAPER WRITER
• RESEARCH PROPOSAL WRITING SERVICES
• SCHOLARSHIP ESSAY HELP
• SPEECH HELP
• STATISTICS HOMEWORK HELP
• TERM PAPER WRITING HELP
• THESIS EDITING SERVICES
• THESIS PROPOSAL WRITING SERVICE
• TRIGONOMETRY HOMEWORK HELP
• ADMISSION ESSAY WRITING HELP
• BIOLOGY PAPER WRITING SERVICE
• BOOK REPORT WRITING HELP
• BUY BOOK REVIEW
• BUY COURSEWORKS
• BUY DISCUSSION POST
• BUY TERM PAPER
• CAPSTONE PROJECT WRITING SERVICE
• COURSEWORK WRITING SERVICE
• CRITIQUE MY ESSAY
• CUSTOM RESEARCH PAPER
• CUSTOMER CONDUCT
• DISSERTATION EDITING SERVICE
• DISSERTATION WRITERS
• DO MY DISSERTATION FOR ME
• DO MY POWERPOINT PRESENTATION
• EDIT MY PAPER
• English Research Paper Writing Service
• ENGLISH RESEARCH PAPER WRITING SERVICE
• ESSAY WRITING HELP
• ESSAYS FOR SALE
• GRADUATE PAPER WRITING SERVICE
• LAW ASSIGNMENT WRITING HELP
• MARKETING ASSIGNMENT WRITING HELP
• NON-PLAGIARIZED ESSAYS
• NURSING ASSIGNMENT HELP
• PAY FOR COURSEWORK
• PAY FOR ESSAYS
• PAY FOR LITERATURE REVIEW
• PAY FOR PAPERS
• PAY FOR RESEARCH PAPERS
• PERSONAL STATEMENT EDITING SERVICE
• PERSONAL STATEMENT WRITER
• PERSUASIVE ESSAY WRITING HELP
• PERSUASIVE ESSAY WRITING SERVICES
• PHD THESIS WRITING SERVICE
• PROOFREAD MY PAPER
• PSYCHOLOGY ESSAY WRITING SERVICES
• THESIS STATEMENT HELP
• WRITE MY ANNOTATED BIBLIOGRAPHY FOR ME
• WRITE MY CASE STUDY
• WRITE MY DISCUSSION BOARD POST
• WRITE MY LAB REPORT

Announcing the NeurIPS 2023 Paper Awards 

Communications Chairs 2023 2023 Conference awards , neurips2023

By Amir Globerson, Kate Saenko, Moritz Hardt, Sergey Levine and Comms Chair, Sahra Ghalebikesabi 

We are honored to announce the award-winning papers for NeurIPS 2023! This year’s prestigious awards consist of the Test of Time Award plus two Outstanding Paper Awards in each of these three categories: 

• Two Outstanding Main Track Papers 
• Two Outstanding Main Track Runner-Ups 
• Two Outstanding Datasets and Benchmark Track Papers  

This year’s organizers received a record number of paper submissions. Of the 13,300 submitted papers that were reviewed by 968 Area Chairs, 98 senior area chairs, and 396 Ethics reviewers 3,540  were accepted after 502 papers were flagged for ethics reviews . 

We thank the awards committee for the main track: Yoav Artzi, Chelsea Finn, Ludwig Schmidt, Ricardo Silva, Isabel Valera, and Mengdi Wang. For the Datasets and Benchmarks track, we thank Sergio Escalera, Isabelle Guyon, Neil Lawrence, Dina Machuve, Olga Russakovsky, Hugo Jair Escalante, Deepti Ghadiyaram, and Serena Yeung. Conflicts of interest were taken into account in the decision process.

Congratulations to all the authors! See Posters Sessions Tue-Thur in Great Hall & B1-B2 (level 1).

Outstanding Main Track Papers

Privacy Auditing with One (1) Training Run Authors: Thomas Steinke · Milad Nasr · Matthew Jagielski

Poster session 2: Tue 12 Dec 5:15 p.m. — 7:15 p.m. CST, #1523

Oral: Tue 12 Dec 3:40 p.m. — 4:40 p.m. CST, Room R06-R09 (level 2)

Abstract: We propose a scheme for auditing differentially private machine learning systems with a single training run. This exploits the parallelism of being able to add or remove multiple training examples independently. We analyze this using the connection between differential privacy and statistical generalization, which avoids the cost of group privacy. Our auditing scheme requires minimal assumptions about the algorithm and can be applied in the black-box or white-box setting. We demonstrate the effectiveness of our framework by applying it to DP-SGD, where we can achieve meaningful empirical privacy lower bounds by training only one model. In contrast, standard methods would require training hundreds of models.

Are Emergent Abilities of Large Language Models a Mirage? Authors: Rylan Schaeffer · Brando Miranda · Sanmi Koyejo

Poster session 6: Thu 14 Dec 5:00 p.m. — 7:00 p.m. CST, #1108

Oral: Thu 14 Dec 3:20 p.m. — 3:35 p.m. CST, Hall C2 (level 1) 

Abstract: Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability , appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher’s choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous, predictable changes in model performance. We present our alternative explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities, (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show how to choose metrics to produce never-before-seen seemingly emergent abilities in multiple vision tasks across diverse deep networks. Via all three analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.

Outstanding Main Track Runner-Ups

Scaling Data-Constrained Language Models Authors : Niklas Muennighoff · Alexander Rush · Boaz Barak · Teven Le Scao · Nouamane Tazi · Aleksandra Piktus · Sampo Pyysalo · Thomas Wolf · Colin Raffel

Poster session 2: Tue 12 Dec 5:15 p.m. — 7:15 p.m. CST, #813

Oral: Tue 12 Dec 3:40 p.m. — 4:40 p.m. CST, Hall C2 (level 1)  

Abstract : The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations .

Direct Preference Optimization: Your Language Model is Secretly a Reward Model Authors: Rafael Rafailov · Archit Sharma · Eric Mitchell · Christopher D Manning · Stefano Ermon · Chelsea Finn

Poster session 6: Thu 14 Dec 5:00 p.m. — 7:00 p.m. CST, #625

Oral: Thu 14 Dec 3:50 p.m. — 4:05 p.m. CST, Ballroom A-C (level 2)  

Abstract: While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF). However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model. In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized exactly with a single stage of policy training, essentially solving a classification problem on the human preference data. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning. Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO exceeds RLHF’s ability to control sentiment of generations and improves response quality in summarization and single-turn dialogue while being substantially simpler to implement and train.

Outstanding Datasets and Benchmarks Papers

In the dataset category : 

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Authors:  Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus C. Will · Gunnar Behrens · Julius Busecke · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah L. Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Tian Zheng · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Mike Pritchard

Poster session 4: Wed 13 Dec 5:00 p.m. — 7:00 p.m. CST, #105 

Oral: Wed 13 Dec 3:45 p.m. — 4:00 p.m. CST, Ballroom A-C (level 2)

Abstract: Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.   

In the benchmark category :

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Authors: Boxin Wang · Weixin Chen · Hengzhi Pei · Chulin Xie · Mintong Kang · Chenhui Zhang · Chejian Xu · Zidi Xiong · Ritik Dutta · Rylan Schaeffer · Sang Truong · Simran Arora · Mantas Mazeika · Dan Hendrycks · Zinan Lin · Yu Cheng · Sanmi Koyejo · Dawn Song · Bo Li

Poster session 1: Tue 12 Dec 10:45 a.m. — 12:45 p.m. CST, #1618  

Oral: Tue 12 Dec 10:30 a.m. — 10:45 a.m. CST, Ballroom A-C (Level 2)

Abstract: Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications to healthcare and finance – where mistakes can be costly. To this end, this work proposes a comprehensive trustworthiness evaluation for large language models with a focus on GPT-4 and GPT-3.5, considering diverse perspectives – including toxicity, stereotype bias, adversarial robustness, out-of-distribution robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. Based on our evaluations, we discover previously unpublished vulnerabilities to trustworthiness threats. For instance, we find that GPT models can be easily misled to generate toxic and biased outputs and leak private information in both training data and conversation history. We also find that although GPT-4 is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more vulnerable given jailbreaking system or user prompts, potentially due to the reason that GPT-4 follows the (misleading) instructions more precisely. Our work illustrates a comprehensive trustworthiness evaluation of GPT models and sheds light on the trustworthiness gaps. Our benchmark is publicly available at https://decodingtrust.github.io/.

Test of Time

This year, following the usual practice, we chose a NeurIPS paper from 10 years ago to receive the Test of Time Award, and “ Distributed Representations of Words and Phrases and their Compositionality ” by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, won. 

Published at NeurIPS 2013 and cited over 40,000 times, the work introduced the seminal word embedding technique word2vec. Demonstrating the power of learning from large amounts of unstructured text, the work catalyzed progress that marked the beginning of a new era in natural language processing.

Greg Corrado and Jeffrey Dean will be giving a talk about this work and related research on Tuesday, 12 Dec at 3:05 – 3:25 pm CST in Hall F.  

Related Posts

2023 Conference

Announcing NeurIPS 2023 Invited Talks

Reflections on the neurips 2023 ethics review process, neurips newsletter – november 2023.

Cookies on GOV.UK

We use some essential cookies to make this website work.

We’d like to set additional cookies to understand how you use GOV.UK, remember your settings and improve government services.

We also use cookies set by other sites to help us deliver content from their services.

You have accepted additional cookies. You can change your cookie settings at any time.

You have rejected additional cookies. You can change your cookie settings at any time.

• Carers and disability benefits

Disability Benefits system to be overhauled as consultation launched on Personal Independence Payment

Government to reform disability benefits system to ensure they’re targeted at those most in need.

• Consultation to be published today on proposals to move away from fixed cash benefit system towards tailored support
• Comes as over 2.6 million people of working age now receiving  PIP  with monthly new claims almost doubling since 2019

Plans to make the disability benefits system fit for the future and overhaul the “one size fits all” approach are set to be published today (Monday 29 April), following the Prime Minister’s speech which set out the government’s wide-ranging ambitions for welfare reform.   

The Modernising Support Green Paper will explore how our welfare system could be redesigned to ensure people with disabilities and long-term health conditions get the support they need to achieve the best outcomes, with an approach that focuses support on those with the greatest needs and extra costs.

The UK’s health landscape has changed since Personal Independence Payment ( PIP ) was introduced in 2013 with the intention that it would be a more sustainable benefit that would support disabled people to live independently by helping with the extra costs they face. 

However, the caseload and costs are now spiralling. There are now 2.6 million people of working age claiming  PIP  and  DLA  – with 33,000 new awards for  PIP  each month which is more than double the rate before the pandemic. This is expected to cost the taxpayer £28 billion a year by 2028/29 – a 110% increase in spending since 2019.

This is in part fuelled by the rise in people receiving  PIP  for mental health conditions such as mixed anxiety and depressive disorders, with monthly awards doubling from 2,200 to 5,300 a month since 2019. 

Since 2015, the proportion of the caseload receiving the highest rate of PIPhas increased from 25% to 36%. And many more people being awarded PIPnow have mental health conditions than when it was first introduced.  

In line with the wider reforms to ensure the welfare system is fair and compassionate, the Modernising Support Green Paper proposals centre on targeting and improving the support for those who need it most.

These ideas include removing the  PIP  assessment altogether for people with certain long term health conditions or disabilities, including those with terminal illnesses to reduce bureaucracy and make life easier for those most in need of support.

By more accurately targeting support, we will ensure the large scale of government expenditure on  PIP  translates into better outcomes for disabled people and those with health conditions. 

Prime Minister Rishi Sunak said:

It’s clear that our disability benefits system isn’t working in the way it was intended, and we’re determined to reform it to ensure it’s sustainable for the future, so we can continue delivering support to those who genuinely need it most.

Today’s Green Paper marks the next chapter of our welfare reforms and is part of our plan to make the benefits system fairer to the taxpayer, better targeted to individual needs and harder to exploit by those who are trying to game the system.

We’re inviting views from across society to ensure everyone has a chance to make their voices heard and shape our welfare reforms.

Work and Pensions Secretary Mel Stride said:   

We’re making the biggest welfare reforms in a generation – protecting those most in need while supporting thousands into work as we modernise our benefit system to reflect the changing health landscape.

A decade on from the introduction of  PIP , this Green Paper opens the next chapter of reform, enhancing the support for people with health conditions and disabilities while ensuring the system is fair to the taxpayer.

The Green Paper sets out proposals across three key priorities to fundamentally reform the system:

Making changes to the eligibility criteria for  PIP , so it is fairer and better targeted

Through previous consultations, we have been told that the criteria currently used in assessments do not always fully reflect how a disability or health condition impacts on a person’s daily life. The criteria have changed over time and no longer capture these different impacts as originally intended.

We will consider whether the current thresholds for entitlement correctly reflect the need for ongoing financial support. This includes considering if current descriptors - such as the need for aids and appliances - are good indicators of extra costs.

We will also look at changing the qualifying period for  PIP  in order to ensure the impact that people’s conditions will have on them over time is fully understood and consider whether we should change the test used to determine if a condition is likely to continue long-term.

Reforming the  PIP  assessment so that it is more closely linked to a person’s condition and exploring removing assessment entirely for those most in need.

PIP  is over a decade old and a lot has changed since the assessment was developed. We know some people continue to find  PIP  assessments difficult and repetitive, and view the assessment as too subjective.

We will consider whether some people could receive  PIP  without needing an assessment by basing entitlement on specific health conditions or disabilities supported by medical evidence.

This includes looking at whether evidence of a formal diagnosis by a medical expert should be a requirement to be assessed as eligible for  PIP . This will make it easier and quicker for people with severe or terminal conditions to get the vital support they need.

We will explore alternative approaches to ensure people are given the right help to fulfil their potential and live independently. The UK has used a fixed cash transfer system since the 1970s but there are a number of international systems that look at the specific extra costs people have and provide more tailored support instead.

For example, in New Zealand, the amount of Disability Allowance is based on a person’s extra costs which are verified by a health practitioner. Norway’s Basic Benefit requires people to provide a letter from a GP outlining the nature of their condition and the associated extra costs. 

We are considering options including one-off grants to better help people with significant costs such as home adaptations or expensive equipment, as well as giving vouchers to contribute towards specific costs, or reimbursing claimants who provide receipts for purchases of aids, appliances or services.

This reflects the fact that some claimants will have significant extra costs related to their disability, and others will have minimal or specific costs.

While these alternative models help people with the extra costs of their disability or health condition, we know other forms of support including health care, social services care provision and respite are also important to help people to realise their full potential and live independently.

We are also considering whether some people receiving  PIP  who have lower, or no extra costs, may have better outcomes from improved access to treatment and support than from a cash payment.

Andy Cook, Chief Executive of the Centre for Social Justice, said:

Our landmark Two Nations report laid bare the lasting impact of the pandemic on our nation’s most vulnerable communities.

With the welfare system now grappling with the combined challenges of economic inactivity, school absence and mental health, this consultation provides a meaningful opportunity to shape the future of Britain’s welfare state.

We owe it to those most struggling to make sure the benefit system provides the best support to those who need it. And with costs skyrocketing, it is time to bring the welfare system into the post-lockdown age.

The Green Paper is the latest of the government’s welfare reforms to ensure disabled people and people with long-term health conditions can live full and independent lives. It builds on last year’s Health and Disability White Paper and the £2.5 billion Back to Work Plan which will break down barriers to work for over one million people.  

The Government is also delivering the largest expansion in mental health services in a generation, with almost £5 billion of extra funding over the past five years, and a near doubling of mental health training places.

Our reforms to the Work Capability Assessment are expected to reduce the number of people put onto the highest tier of incapacity benefits by 424,000, people who will now receive personalised support to prepare for work, while our Chance to Work Guarantee will mean people can try work without fear of losing their benefits. 

Further Information

• The consultation can be found here: Modernising support for independent living: the health and disability green paper - GOV.UK (www.gov.uk)
• This consultation will be open for 12 weeks and we are inviting views from across society to ensure everyone has a chance to shape the modernisation of the welfare system. The findings of the consultation, which closes on Tuesday 23 July, will inform future reforms.
• The UK Government is committed to improving the lives of disabled people and people with long-term health conditions in all parts of the UK.
• In Wales, Personal Independence Payment ( PIP ) is the responsibility of the UK Government.
• In Northern Ireland,  PIP  is transferred and is the responsibility of the Department for Communities.
• In Scotland, Adult Disability Payment ( ADP ) has replaced  PIP  and is the responsibility of the Scottish Government. The transfer of existing Scottish  PIP  claimants from  DWP  to Social Security Scotland started in summer 2022 and will continue until 2025.
• We will continue to work with the Devolved Administrations to consider the implications of the proposals in this Green Paper in Scotland, Wales and Northern Ireland.

Share this page

The following links open in a new tab

• Share on Facebook (opens in new tab)
• Share on Twitter (opens in new tab)

Is this page useful?

• Yes this page is useful
• No this page is not useful

Help us improve GOV.UK

Don’t include personal or financial information like your National Insurance number or credit card details.

To help us improve GOV.UK, we’d like to know more about your visit today. We’ll send you a link to a feedback form. It will take only 2 minutes to fill in. Don’t worry we won’t send you spam or share your email address with anyone.

• Support Home
• Printer Support
• Computer Support
• Poly Collaboration Devices
• Ink & Toner Cartridges
• Software and Drivers
• Printer Diagonostic Tools
• Computer Diagnostic Tools
• Windows Support
• Print and Scan Doctor
• HP PC Hardware Diagnostics
• Create a new case
• Find an existing case
• Manage contracts and warranties
• Register an account
• MyHPSupport Help
• Security Bulletins
• Support Dashboard
• HP Account benefits

HP PCs – Support for Windows 11

In June 2021, Microsoft introduced the newest Windows ® operating system, Windows 11.

For more information Windows 11, see Upgrade to the New Windows 11 OS | Microsoft .

How do I update Windows 11 to the latest version?

Windows 11 update was released in September 2022 and will continue to roll out to Windows 11 PCs.

For more information about updating Windows 11, see HP PCs - Installing the latest version of Windows 11 .

What are the new features in the latest version of Windows 11?

The latest version of Windows 11 includes new security features, Start Menu customization, tools, and app support.

For more information, see Upgrade to the New Windows 11 OS | Microsoft .

What is Smart App Control?

Smart App Control provides enhanced protection from untrusted apps. If Smart App Control spots a malicious or untrusted application, Smart App Control blocks that application to protect your computer. Some apps might be blocked from installation if Smart App Control is enabled. After you disable Smart App Control, it can be re-enabled only after reinstalling Windows or performing a factory reset.

What is Core Isolation and Memory Integrity?

The latest version of Windows 11 features new device security features that protect the core parts of the computer. Using Virtualization Based Security (VBS), Windows isolates the computer memory, which prevents attacks from inserting malicious code into high-security processes. This feature is enabled in the Windows Security app.

Some HP products might have applications with features that do not work with VBS enabled. These features include overclocking features in OMEN Gaming Hub, fan control in HP Command Center, and others. For more information, see HP Consumer Notebook and Desktop PCs - Certain features in HP applications might not function due to Virtualization-Based Security (VBS) being enabled .

Enabling VBS on the computer can affect the overall performance of the computer when using graphics or processor intensive applications. To enable or disable VBS in BIOS Setup, see HP PCs - Enable Virtualization Technology in the BIOS .

What is the difference between Windows 10 and Windows 11?

Windows 11 has all the power and security of Windows 10 with a redesigned and refreshed look. It also comes with new tools, sounds, and apps. Every detail has been considered. All of it comes together to bring you a refreshing experience on your PC.

For more information, see Introducing Windows 11 | Microsoft (in English) and HP PCs - Understanding and using Windows 11 .

How do I install Windows 11?

There are several different ways to install Windows 11 including using Windows Update, creating Windows 11 installation media, or creating an installation image.

When your PC is eligible for the Windows 11 upgrade, Microsoft sends a notification in Windows Update. For more information on different ways to install Windows 11, see Ways to install Windows 11 (Microsoft) (in English). For instructions on installing Windows 11, see one of the following options:

HP PCs - Upgrading to Windows 11 using Windows Update

HP PCs - Installing Windows 11 from a USB flash drive

What are the minimum system requirements for Windows 11?

The minimum system requirements for Windows 11 are available on the Microsoft website.

See Windows 11 Specifications - Microsoft (in English) for the minimum system requirements.

How do I know if my PC meets Windows 11 minimum system requirements?

Use Microsoft’s PC Heath Check to determine if your PC meets the minimum system requirements.

For more information, see Upgrade to the New Windows 11 OS | Microsoft (in English).

What do I do if PC Health Check says my PC can’t run Windows 11?

Based on your location and the model of your computer, you might need to enable the Trusted Platform Module (TPM) to meet the minimum system requirements.

For more information about enabling TPM on your computer, see How do I enable TPM on my computer? .

Specific models of HP computers were sold with TPM 1.0 and cannot be upgraded to TPM 2.0.

How do I enable TPM on my computer?

To upgrade to Windows 11, you might need to enable TPM on your computer by following these instructions.

Turn on the computer and press the f10 key to start to the BIOS setup menu.

On the Security tab, check whether your TPM Device status is Hidden .

Toggle the TPM device status to Available .

Change TPM State to Enabled .

Press f10 to exit, and then click Yes to save changes. If prompted, press f1 to confirm the changes and restart the computer.

What if my device doesn’t meet the minimum hardware specifications?

Devices that do not meet the minimum system requirements will remain on Windows 10 and continue to be supported with security updates.

Customers using long term service releases (LTSC and LTSB) will continue to be supported through those published end of support dates.

For more information about Windows 10 support, see HP products tested with Windows 10 | HP® Customer Support .

Will my printer work with Windows 11?

If your printer worked with Windows 10, it should continue to work with Windows 11.

For more information, see HP printer does not work after upgrading to Windows 11 and Windows 11 compatible printers .

When can I buy a new PC with Windows 11?

HP notebooks and desktops, for personal and business use, are available with a preinstalled Windows 11 operating system.

While some new devices will be sold with Windows 11 preinstalled, other new devices will need to upgrade to Windows 11.

Will the upgrade to Windows 11 be free?

Windows 10 devices that meet the Windows 11 minimum system requirements will be eligible for the free upgrade.

If I buy a new PC now, will it be eligible for the free upgrade?

New Windows 10 devices that meet the Windows 11 minimum system requirements will be eligible for the free upgrade.

Will availability of the Windows 11 upgrade be staggered?

Upgrades to Windows 11 will begin to roll out late in 2021 and continue into 2022.

HP notebooks and desktops, for personal and business use, will be available with Windows 11 preinstalled starting later this year.

Most HP devices that meet the minimum system requirement will be eligible for the upgrade when Windows 11 is available starting later this year.

Some older HP devices that meet the minimum system requirements will be eligible for upgrade in 2022.

How will I know when the upgrade is available for my Windows 10 PC?

Windows Update provides a notification when your PC is eligible for upgrade to Windows 11. You can check to see if your device is ready by going to Settings > Windows Update .

Do I have to upgrade to Windows 11?

When Windows Update displays that the upgrade is ready for the device, you can choose whether to install it. If you decline the upgrade, you can still choose to upgrade via Windows Update in Settings.

How long will the free upgrade offer last?

The free upgrade offer does not have a specific end date for eligible systems. However, Windows reserves the right to eventually end support for the free offer. This end date will be no sooner than one year from general availability.

Will HP support customers through the upgrade process?

Yes. HP has worked closely with Microsoft to ensure a trouble-free upgrade experience.

Depending on the device’s hardware configuration (processor, storage, memory) the time to perform the upgrade varies.

What version of Windows 10 is required to receive the upgrade?

The device must be running Windows 10 version 20H1 or later to receive the upgrade offer. If your device is running a previous version of Windows 10, you must update your device to Windows 10 version 20H1 or later.

Does Windows 11 take up more space on my PC than Windows 10?

No. Windows 11 and Windows 10 require approximately the same amount of disk space. During the upgrade process, however, extra space is required. Windows will clean up this extra disk space about ten days after the upgrade is complete.

How long does it take to install Windows 11?

Downloading and installing Windows 11 will most likely take longer than a typical Windows 10 feature update. You can use your PC while you’re downloading the operating system, and then you have the option to schedule the installation at a specific time when you aren’t planning on using your PC.

The time that it takes to download and upgrade a device depends on a number of factors, including bandwidth and system specifications like memory and storage. The upgrade is currently estimated to be over 3 GB.

Can I upgrade my Windows 10 PC to Windows 11 if I’m running S mode?

If your PC meets the minimum hardware specifications, the Windows 10 Home edition in S mode can upgrade to Windows 11 Home edition in S mode. The only Windows 11 edition with S Mode is the Home Edition. If you use Windows 10 Home in S Mode, you will be able to upgrade directly to Windows 11 from Windows Update and remain in S Mode.

For other editions, such as Windows 10 Pro, you will need to exit S Mode to upgrade to Windows 11 and will not have the option to switch back into S Mode.

When I upgrade to Windows 11, what will happen to my files?

By default, all your files and data will transfer to Windows 11. However, HP recommends backing up your files before installation.

For more information, see HP PCs - Back up your files (Windows 11, 10) .

Can I go back to Windows 10 if I don’t like Windows 11?

Yes. After you have installed Windows 11, there is a 10-day period where you can switch back to Windows 10 while keeping your files and data. After the 10-day period, you’ll need to back up your data and do a clean install or full recovery to return to Windows 10.

Will my accessories work with Windows 11?

If your accessories worked with Windows 10 and meet the Windows 11 requirements, they should work with Windows 11.

What is the upgrade path from Windows 10 to Windows 11?

HP recommends that you upgrade your device via the Windows Update app in Windows 10. However, if you are an advanced user, you will have the option to upgrade or do a clean install of Windows 11 using media from Microsoft.

Can I upgrade my Windows 7 device to Windows 11?

For Windows 7 devices that meet the hardware requirements, you will need to do a clean install of Windows 11. A Windows 10 activation will be required to upgrade for free.

Will Windows IoT also upgrade to Windows 11? If so, is it following the same timeline?

More information about Windows IoT Enterprise Edition will be available at a later date.

Additional support options

Enter a topic to search our knowledge library

• Share full article

Advertisement

Supported by

Guest Essay

Liz Cheney: The Supreme Court Should Rule Swiftly on Trump’s Immunity Claim

By Liz Cheney

Ms. Cheney, a Republican, is a former U.S. representative from Wyoming and was vice chairwoman of the Jan. 6 select committee in the House of Representatives.

On Thursday, the U.S. Supreme Court will hear Donald Trump’s arguments that he is immune from prosecution for his efforts to steal the 2020 presidential election. It is likely that all — or nearly all — of the justices will agree that a former president who attempted to seize power and remain in office illegally can be prosecuted. I suspect that some justices may also wish to clarify whether doctrines of presidential immunity might apply in other contexts — for example, to a president’s actions as commander in chief during a time of war. But the justices should also recognize the profoundly negative impact they may have if the court does not resolve these issues quickly and decisively.

If delay prevents this Trump case from being tried this year, the public may never hear critical and historic evidence developed before the grand jury, and our system may never hold the man most responsible for Jan. 6 to account.

The Jan. 6 House select committee’s hearings and final report in 2022 relied on testimony given by dozens of Republicans — including many who worked closely with Mr. Trump in the White House, in his Justice Department and on his 2020 presidential campaign. The special counsel Jack Smith’s election-related indictment of Mr. Trump relies on many of the same firsthand witnesses. Although the special counsel reached a number of the same conclusions as the select committee, the indictment is predicated on a separate and independent investigation. Evidence was developed and presented to a grand jury sitting in Washington, D.C.

The indictment and public reporting suggest that the special counsel was able to obtain key evidence our committee did not have. For example, it appears that the grand jury received evidence from witnesses such as Mark Meadows, a former Trump chief of staff, and Dan Scavino, a former Trump aide, both of whom refused to testify in our investigation. Public reporting also suggests that members of Mr. Trump’s Office of White House Counsel and other White House aides testified in full, without any limitations based on executive privilege, as did Vice President Mike Pence and his counsel.

The special counsel’s indictment lays out Mr. Trump’s detailed plan to overturn the 2020 election, including the corrupt use of fraudulent slates of electors in several states. According to the indictment, senior advisers in the White House, Justice Department and elsewhere repeatedly warned that Mr. Trump’s claims of election fraud were false and that his plans for Jan. 6 were illegal. Mr. Trump chose to ignore those warnings. (Remember what the White House lawyer Eric Herschmann told Mr. Trump’s alleged co-conspirator John Eastman on Jan. 7, 2021: “Get a great f’ing criminal defense lawyer. You’re gonna need it.”) There is little doubt that Mr. Trump’s closest advisers also gave the federal grand jury minute-to-minute accounts of his malicious conduct on Jan. 6, describing how they repeatedly begged the president to instruct the violent rioters to leave our Capitol and how Mr. Trump refused for several hours to do so as he watched the attack on television. This historic testimony about a former president’s conduct is likely to remain secret until the special counsel presents his case at trial.

As a criminal defendant, Mr. Trump has long had access to federal grand jury material relating to his Jan. 6 indictment and to all the testimony obtained by our select committee. He knows what all these witnesses have said under oath and understands the risks he faces at trial. That’s why he is doing everything possible to try to delay his Jan. 6 federal criminal trial until after the November election. If the trial is delayed past this fall and Mr. Trump wins re-election, he will surely fire the special counsel, order his Justice Department to drop all Jan. 6 cases and try to prevent key grand jury testimony from ever seeing the light of day.

I know how Mr. Trump’s delay tactics work. Our committee had to spend months litigating his privilege claims (in Trump v. Thompson) before we could gain access to White House records. Court records and public reporting suggest that the special counsel also invested considerable time defeating Mr. Trump’s claims of executive privilege, which were aimed at preventing key evidence from reaching the grand jury. All of this evidence should be presented in open court, so that the public can fully assess what Mr. Trump did on Jan. 6 and what a man capable of that type of depravity could do if again handed the awesome power of the presidency.

Early this year, a federal appeals court took less than a month after oral argument to issue its lengthy opinion on immunity. History shows that the Supreme Court can act just as quickly , when necessary. And the court should fashion its decision in a way that does not lead to further time-consuming appeals on presidential immunity. It cannot be that a president of the United States can attempt to steal an election and seize power but our justice system is incapable of bringing him to trial before the next election four years later.

Mr. Trump believes he can threaten and intimidate judges and their families , assert baseless legal defenses and thereby avoid accountability altogether. Through this conduct, he seeks to break our institutions. If Mr. Trump’s tactics prevent his Jan. 6 trial from proceeding in the ordinary course, he will also have succeeded in concealing critical evidence from the American people — evidence demonstrating his disregard for the rule of law, his cruelty on Jan. 6 and the deep flaws in character that make him unfit to serve as president. The Supreme Court should understand this reality and conclude without delay that no immunity applies here.

Liz Cheney, a Republican, is a former U.S. representative from Wyoming and was vice chairwoman of the Jan. 6 select committee in the House of Representatives.

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

Follow the New York Times Opinion section on Facebook , Instagram , TikTok , WhatsApp , X and Threads .

AMD Software: Adrenalin Edition 24.4.1 Release Notes

Article Number: RN-RAD-WIN-24-4-1

New Feature Highlights

New game support.

• Manor Lords
• Gray Zone Warfare Early Access

Performance Highlights

• Performance improvements for HELLDIVERS™ 2

Expanded HYPR-Tune Support

• Nightingale
• SKULL AND BONES™

Expanded Vulkan Extensions Support

•   VK_KHR_shader_maximal_reconvergence
• VK_KHR_dynamic_rendering_local_read
• Click HERE  for more information about other Vulkan® extension support.

AI Application Update

• Support and optimizations for 7.1.0 & 7.1.1 versions of Topaz Gigapixel AI application with new “Recovery” and “Low Resolution” AI upscaling features.

Fixed Issues

• Performance improvements for HELLDIVERS™ 2.
• Intermittent application crash may be observed while playing Lords of the Fallen and entering certain areas on Radeon™ RX 6000 series graphics products. 
• Artifacts may appear in certain mud environments while playing SnowRunner on some AMD Graphics Products, such as the Radeon™ RX 6800. 
• Rainbow-like artifacts may appear in water environments while playing Horizon Forbidden West™ Complete Edition on Radeon™ RX 6000 series graphics products.
• Intermittent application crash or driver timeout may be observed while playing Overwatch® 2 with Radeon™ Boost enabled on Radeon™ RX 6000 and above series graphics products. 
• Intermittent application freeze when first launching SteamVR using the Quest Link feature on Meta Quest 2.
• Intermittent system or application crash when screen sharing using Microsoft Teams. 
• Intermittent application crash changing Anti-Aliasing settings while playing Enshrouded on Radeon™ 7000 series graphics products. 
• Display colors may appear “dim” or “washed out” after closing Enshrouded with Auto HDR enabled.

Known Issues

• Black corruption may be observed while playing Alien Isolation on Radeon™ 7000 series graphics products.
• Corruption may be observed while playing Dying Light 2 Stay Human: Reloaded Edition or Alan Wake 2 with Radeon™ Boost enabled. Users experiencing this issue are recommended to disable Radeon™ Boost as a temporary workaround.
• Max Payne 1 and 2 may fail to launch on RDNA 3 series graphics products when Anti-Aliasing is enabled.
• Texture flicking may be observed while playing Hitman: Contracts.[Resolution targeted for 24.5.1]
• Intermittent stutter immediately after alt-tab with Performance Metrics Overlay enabled. [Resolution targeted for 24.5.1]
• Audio and video may intermittently become out of sync while recording using the AV1 codec in AMD Software: Adrenalin Edition. [Resolution targeted for Q3]

Package Contents

AMD Software: Adrenalin Edition 24.4.1 Driver Version 23.40.31.05 for Windows® 10 and Windows® 11 (Windows Driver Store Version 31.0.24031.5001).

The AMD Software: Adrenalin Edition 24.4.1 installation package can be downloaded from the following link:

By clicking the Download button, you are confirming that you have read and agreed to be bound by the terms and conditions of the  End User License Agreement  (“EULA”).  If you do not agree to the terms and conditions of these licenses, you do not have a license to any of the AMD software provided by this download.

• AMD Software: Adrenalin Edition 24.4.1 Driver for Windows® 10 & Windows® 11 64-bit

Systems pairing RDNA series graphics products with Polaris or Vega series graphics products:

• AMD Software: Adrenalin Edition 24.4.1 Driver Including Vega and Polaris Series Graphics Support for Windows® 10 & Windows® 11 64-bit

Installing AMD Software: Adrenalin Edition

For detailed instructions on how to correctly uninstall or install AMD Software: Adrenalin Edition, please refer to the following support resources:

• How-To Uninstall AMD Software on a Windows® Based System
• How-To Install AMD Software on a Windows® Based System

NOTE : This driver is not intended for use on AMD Radeon products running in Apple Boot Camp platforms. Users of these platforms should contact their system manufacturer for driver support. When installing AMD Software: Adrenalin Edition 24.4.1 for the Windows® operating system, the user must be logged on as Administrator, or have Administrator rights to complete the installation of AMD Software: Adrenalin Edition 24.4.1. 

Radeon Product Compatibility

AMD Software: Adrenalin Edition 24.4.1 is compatible with the following AMD Radeon products.

Mobility Radeon™ Product Compatibility

AMD Software: Adrenalin Edition 24.4.1 is a notebook reference graphics driver with limited support for system vendor specific features. 

​​​​AMD Processors with Radeon Graphics Product Compatibility

Important note for laptop and all-in-one (aio) pcs .

AMD recommends OEM-provided drivers which are customized and validated for their system-specific features and optimizations. If you experience issues using the AMD Software: Adrenalin Edition driver package downloaded from AMD.com, please install the OEM-provided drivers for full support and compatibility. AMD Software: Adrenalin Edition does not include support for handheld gaming devices.  Users should check with the OEM for device specific drivers.

Compatible Operating Systems

AMD Software: Adrenalin Edition 24.4.1 is designed to support the following Microsoft® Windows® platforms. Operating System support may vary depending on your specific AMD Radeon product.

• Windows 11 version 21H2 and later
• Windows 10 64-bit version 1809 and later

WHQL Results

​​​© 2024 Advanced Micro Devices, Inc.  All Rights Reserved.

The information contained herein is for informational purposes only and is subject to change without notice. While every precaution has been taken in the preparation of this document, it may contain technical inaccuracies, omissions and typographical errors, and AMD is under no obligation to update or otherwise correct this information. Advanced Micro Devices, Inc. makes no representations or warranties with respect to the accuracy or completeness of the contents of this document and assumes no liability of any kind, including the implied warranties of non-infringement, merchantability or fitness for particular purposes, with respect to the operation or use of AMD hardware, software or other products described herein. No license, including implied or arising by estoppel, to any intellectual property rights is granted by this document. This notice does not change the terms and limitations applicable to the purchase or use of AMD's products that may be set forth in a separate signed agreement between you and AMD.

OVERCLOCKING WARNING:  AMD processors are intended to be operated only within their associated specifications and factory settings.  Operating your AMD processor outside of official AMD specifications or outside of factory settings, including but not limited to the conducting of overclocking (including use of this overclocking software, even if such software has been directly or indirectly provided by AMD or otherwise affiliated in any way with AMD), may damage your processor and/or lead to other problems, including but not limited to, damage to your system components (including your motherboard and components thereon (e.g. memory)), system instabilities (e.g. data loss and corrupted images), reduction in system performance, shortened processor, system component and/or system life and in extreme cases, total system failure.  AMD does not provide support or service for issues or damages related to use of an AMD processor outside of official AMD specifications or outside of factory settings.  You may also not receive support or service from your board or system manufacturer. Please make sure you have saved all important data before using this overclocking software.  DAMAGES CAUSED BY USE OF YOUR AMD PROCESSOR OUTSIDE OF OFFICIAL AMD SPECIFICATIONS OR OUTSIDE OF FACTORY SETTINGS ARE NOT COVERED UNDER ANY AMD PRODUCT WARRANTY AND MAY NOT BE COVERED BY YOUR BOARD OR SYSTEM MANUFACTURER’S WARRANTY.

The software that has been directly or indirectly provided by AMD or an entity otherwise affiliated with AMD may disable or alter: (1) software including features and functions in the operating system, drivers and applications, and other system settings; and (2) system services.  WHEN THE SOFTWARE IS USED TO DISABLE OR ALTER THESE ITEMS IN WHOLE OR PART, YOU MAY EXPERIENCE (A) INCREASED RISKS THAT CERTAIN SECURITY FUNCTIONS DO NOT FUNCTION THEREBY EXPOSING YOUR COMPUTER SYSTEM TO POTENTIAL SECURITY THREATS INCLUDING, WITHOUT LIMITATION, HARM FROM VIRUSES, WORMS AND OTHER HARMFUL SOFTWARE; (B) PERFORMANCE AND INTEROPERABILITY ISSUES THAT MAY ADVERSELY AFFECT YOUR EXPERIENCE AND THE STABILITY OF YOUR COMPUTING SYSTEM; AND (C) OTHER EXPERIENCES RESULTING IN ADVERSE EFFECTS, INCLUDING, BUT NOT LIMITED, TO DATA CORRUPTION OR LOSS.

AMD, the AMD Arrow logo, AMD Radeon, AMD Freesync and combinations thereof are trademarks of Advanced Micro Devices, Inc.

Windows and DirectX are registered trademarks of Microsoft Corporation.

Vulkan and the Vulkan logo are registered trademarks of the Khronos Group Inc.

Other names used herein are for identification purposes only and may be trademarks of their respective companies.