Automatic Essay Grading

Postsecondary Learning

How U of Michigan Built Automated Essay-Scoring Software to Fill ‘Feedback Gap’ for Student Writing

By Jessica Leigh Brown     Jun 6, 2017

The University of Michigan’s M-Write program is built on the idea that students learn best when they write about what they’re studying, rather than taking multiple-choice tests. The university has created a way for automated software to give students in large STEM courses feedback on their writing in cases where professors don’t have time to grade hundreds of essays.

The M-Write program started in 2015 as a way to give more writing feedback to students by enlisting other students to serve as peer mentors to help with revisions. This fall, the program will add automated text analysis, or ATA, to its toolbox, primarily to identify students who need extra help.

Senior lecturer Brenda Gunderson teaches a statistics course that will be first to adopt the automated element of M-Write. “It’s a large gateway course with about 2,000 students enrolled every semester,” Gunderson says. “We always have written exams, but it never hurts to have students communicate more through writing.”

As part of the M-Write program, Gunderson introduced a series of writing prompts in the course last year. The prompts are targeted to elicit specific responses that clearly indicate how well students grasp the concepts covered in class. Students who chose to participate in the program completed the writing assignments, submitted them electronically, and received three of their peers’ assignments for review. “We also hired students who’d previously done well in the course as writing fellows,” Gunderson says. “Each fellow is assigned to a group of students and is available to help them with the revision process.”

Rising senior Brittany Tang has been a writing fellow in the M-Write program for the past three semesters. “Right now, I have 60 students in two lab sections,” she says. “After every semester, professors and fellows review every student submission from the class and score them based on a rubric.”

To build the automated system, a software development team used that data to create course-specific algorithms that can identify students who are struggling to understand concepts.

“In developing this ATA system, we needed to go through the pilot project and have students do the writing assignments to collect the data,” Gunderson says. “This fall, we’ll be ready to roll out the program to all the students in the course.” Gunderson is also incorporating eCoach, a personalized student messaging system developed by a research team at U-M, to provide students with targeted advice based on their performance.

When a student submits a writing assignment, the ATA system will generate a score. After a writing fellow quickly reviews it, the score gets delivered to the student via the eCoach system. The student then has an opportunity to revise and resubmit the piece based on the combination of feedback from the assigned writing fellow, the ATA system, and peer review.

Filling the Feedback Gap

The university’s launch of ATA is part of a growing nationwide trend in both K-12 and higher education classrooms, according to Joshua Wilson, assistant professor of education at the University of Delaware. Wilson researches the application of automated essay scoring. “I project the fastest adoption in the K-12 arena, and pretty quick adoption at community colleges, where it is helpful for remedial English courses,” Wilson says. “U-M presents a really interesting model of adoption. It has required them to build a content-specific system, but there’s really a demand for that among faculty who aren’t trained to teach writing.”

Wilson says ATA’s critics dislike the systems because they seem to remove the human element from essay grading—a traditionally personal act. But in reality, systems are being “taught” how to respond by their human programmers. “Systems are designed by looking closely at a large body of representative student work and the strengths and weaknesses of those papers,” he says. “Essentially, they provide a subset to the computer and they develop a model used to evaluate future papers.”

While a computer program will never give the same depth of feedback a professor can, Wilson says these systems could fill a growing gap in many K-12 and higher education classrooms. “I think people who outright reject these systems forget what the status quo is. Unfortunately, we know that instructors don’t give enough feedback, often because the teacher-student ratio is such that they don’t have time.”

In Wilson’s view, ATA feedback isn’t as good as human feedback, but it’s better than nothing—and the quality is improving all the time. “Obviously, a computer can’t understand language the same way we can, but it can identify lexical proxies that, combined with machine learning, can produce a score that’s very consistent with a score given by humans, even though humans are reading it in a different way.”

Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a method of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades—for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification.

Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost.

The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test).


Most historical summaries of AES trace the origins of the field to the work of Ellis Batten Page.[1][2][3][4][5][6][7] In 1966, he argued[8] for the possibility of scoring essays by computer, and in 1968 he published[9] his successful work with a program called Project Essay Grade™ (PEG™). Using the technology of that time, computerized essay scoring would not have been cost-effective,[10] so Page abated his efforts for about two decades.

By 1990, desktop computers had become so powerful and so widespread that AES was a practical possibility. As early as 1982, a UNIX program called Writer's Workbench was able to offer punctuation, spelling, and grammar advice.[11] In collaboration with several companies (notably Educational Testing Service), Page updated PEG and ran some successful trials in the early 1990s.[12]

Peter Foltz and Thomas Landauer developed a system using a scoring engine called the Intelligent Essay Assessor™ (IEA). IEA was first used to score essays in 1997 for their undergraduate courses.[13] It is now a product from Pearson Educational Technologies and used for scoring within a number of commercial products and state and national exams.

IntelliMetric® is Vantage Learning's AES engine. Its development began in 1996.[14] It was first used commercially to score essays in 1998.[15]

Educational Testing Service offers e-rater®, an automated essay scoring program. It was first used commercially in February 1999.[16] Jill Burstein was the team leader in its development. ETS's CriterionSM Online Writing Evaluation Service uses the e-rater engine to provide both scores and targeted feedback.

Lawrence Rudner has done some work with Bayesian scoring, and developed a system called BETSY (Bayesian Essay Test Scoring sYstem).[17] Some of his results have been published in print or online, but no commercial system incorporates BETSY as yet.

Under the leadership of Howard Mitzel and Sue Lottridge, Pacific Metrics developed a constructed response automated scoring engine, CRASE®. Currently utilized by several state departments of education and in a U.S. Department of Education-funded Enhanced Assessment Grant, Pacific Metrics’ technology has been used in large-scale formative and summative assessment environments since 2007.

Measurement Inc. acquired the rights to PEG in 2002 and has continued to develop it.[18]

In 2012, the Hewlett Foundation sponsored a competition on Kaggle called the Automated Student Assessment Prize (ASAP).[19] 201 challenge participants attempted to predict, using AES, the scores that human raters would give to thousands of essays written to eight different prompts. The intent was to demonstrate that AES can be as reliable as human raters, or more so. This competition also hosted a separate demonstration among 9 AES vendors on a subset of the ASAP data. Although the investigators reported that the automated essay scoring was as reliable as human scoring,[20][21] this claim was not substantiated by any statistical tests because some of the vendors required that no such tests be performed as a precondition for their participation.[22] Moreover, the claim that the Hewlett Study demonstrated that AES can be as reliable as human raters has since been strongly contested,[23][24] including by Randy E. Bennett, the Norman O. Frederiksen Chair in Assessment Innovation at the Educational Testing Service.[25] Some of the major criticisms of the study have been that five of the eight datasets consisted of paragraphs rather than essays, four of the eight data sets were graded by human readers for content only rather than for writing ability, and that rather than measuring human readers and the AES machines against the "true score", the average of the two readers' scores, the study employed an artificial construct, the "resolved score", which in four datasets consisted of the higher of the two human scores if there was a disagreement. This last practice, in particular, gave the machines an unfair advantage by allowing them to round up for these datasets.[23]


From the beginning, the basic procedure for AES has been to start with a training set of essays that have been carefully hand-scored.[26] The program evaluates surface features of the text of each essay, such as the total number of words, the number of subordinate clauses, or the ratio of uppercase to lowercase letters—quantities that can be measured without any human insight. It then constructs a mathematical model that relates these quantities to the scores that the essays received. The same model is then applied to calculate scores of new essays.

Recently, one such mathematical model was created by Isaac Persing and Vincent Ng.[27] which not only evaluates essays on the above features, but also on their argument strength. It evaluates various features of the essay, such as the agreement level of the author and reasons for the same, adherence to the prompt's topic, locations of argument components (major claim, claim, premise), errors in the arguments, cohesion in the arguments among various other features. In contrast to the other models mentioned above, this model is closer in duplicating human insight while grading essays.

The various AES programs differ in what specific surface features they measure, how many essays are required in the training set, and most significantly in the mathematical modeling technique. Early attempts used linear regression. Modern systems may use linear regression or other machine learning techniques often in combination with other statistical techniques such as latent semantic analysis[28] and Bayesian inference.[17]

Criteria for success[edit]

Any method of assessment must be judged on validity, fairness, and reliability.[29] An instrument is valid if it actually measures the trait that it purports to measure. It is fair if it does not, in effect, penalize or privilege any one class of people. It is reliable if its outcome is repeatable, even when irrelevant external factors are altered.

Before computers entered the picture, high-stakes essays were typically given scores by two trained human raters. If the scores differed by more than one point, a third, more experienced rater would settle the disagreement. In this system, there is an easy way to measure reliability: by inter-rater agreement. If raters do not consistently agree within one point, their training may be at fault. If a rater consistently disagrees with whichever other raters look at the same essays, that rater probably needs more training.

Various statistics have been proposed to measure inter-rater agreement. Among them are percent agreement, Scott's π, Cohen's κ, Krippendorf's α, Pearson's correlation coefficient r, Spearman's rank correlation coefficient ρ, and Lin's concordance correlation coefficient.

Percent agreement is a simple statistic applicable to grading scales with scores from 1 to n, where usually 4 ≤ n ≤ 6. It is reported as three figures, each a percent of the total number of essays scored: exact agreement (the two raters gave the essay the same score), adjacent agreement (the raters differed by at most one point; this includes exact agreement), and extreme disagreement (the raters differed by more than two points). Expert human graders were found to achieve exact agreement on 53% to 81% of all essays, and adjacent agreement on 97% to 100%.[30][31]

Inter-rater agreement can now be applied to measuring the computer's performance. A set of essays is given to two human raters and an AES program. If the computer-assigned scores agree with one of the human raters as well as the raters agree with each other, the AES program is considered reliable. Alternatively, each essay is given a "true score" by taking the average of the two human raters' scores, and the two humans and the computer are compared on the basis of their agreement with the true score.

Some researchers have reported that their AES systems can, in fact, do better than a human. Page made this claim for PEG in 1994.[12] Scott Elliot said in 2003 that IntelliMetric typically outperformed human scorers.[14] AES machines, however, appear to be less reliable than human readers for any kind of complex writing test.[32][33][34]

In current practice, high-stakes assessments such as the GMAT are always scored by at least one human. AES is used in place of a second rater. A human rater resolves any disagreements of more than one point.[35]


AES has been criticized on various grounds. Yang et al. mention "the overreliance on surface features of responses, the insensitivity to the content of responses and to creativity, and the vulnerability to new types of cheating and test-taking strategies."[35] Several critics are concerned that students' motivation will be diminished if they know that no human will read their writing.[36][37][38] Among the most telling critiques are reports of intentionally gibberish essays being given high scores.[39]

HumanReaders.Org Petition[edit]

On March 12, 2013, HumanReaders.Org launched an online petition, "Professionals Against Machine Scoring of Student Essays in High-Stakes Assessment". Within weeks, the petition gained thousands of signatures, including Noam Chomsky,[40] and was cited in a number of newspapers, including The New York Times,[41][42][43] and on a number of education and technology blogs.[44][45]

The petition describes the use AES for high-stakes testing as "trivial", "reductive", "inaccurate", "undiagnostic", "unfair", and "secretive".[46]

In a detailed summary of research on AES, the petition site notes, "RESEARCH FINDINGS SHOW THAT no one—students, parents, teachers, employers, administrators, legislators—can rely on machine scoring of essays ... AND THAT machine scoring does not measure, and therefore does not promote, authentic acts of writing."[47][48]

The petition specifically addresses the use of AES for high-stakes testing and says nothing about other possible uses.


Most resources for automated essay scoring are proprietary.

  • eRater – Published by ETS
  • Intellimetric – by Vantage Learning
  • Project Essay Grade[49] – by Measurement, Inc.
  • PaperRater.


  1. ^Page, E.B. (2003). "Project Essay Grade: PEG", p. 43. In: Automated Essay Scoring: A Cross-Disciplinary Perspective. Shermis, Mark D., and Jill Burstein, eds. Lawrence Erlbaum Associates, Mahwah, New Jersey, ISBN 0805839739
  2. ^Larkey, Leah S., and W. Bruce Croft (2003). "A Text Categorization Approach to Automated Essay Grading", p. 55. In: Automated Essay Scoring: A Cross-Disciplinary Perspective. Shermis, Mark D., and Jill Burstein, eds. Lawrence Erlbaum Associates, Mahwah, New Jersey, ISBN 0805839739
  3. ^Keith, Timothy Z. (2003). "Validity of Automated Essay Scoring Systems", p. 153. In: Automated Essay Scoring: A Cross-Disciplinary Perspective. Shermis, Mark D., and Jill Burstein, eds. Lawrence Erlbaum Associates, Mahwah, New Jersey, ISBN 0805839739
  4. ^Shermis, Mark D., Jill Burstein, and Claudia Leacock (2006). "Applications of Computers in Assessment and Analysis of Writing", p. 403. In: Handbook of Writing Research. MacArthur, Charles A., Steve Graham, and Jill Fitzgerald, eds. Guilford Press, New York, ISBN 1-59385-190-1
  5. ^Attali, Yigal, Brent Bridgeman, and Catherine Trapani (2010). "Performance of a Generic Approach in Automated Essay Scoring", p. 4. Journal of Technology, Learning, and Assessment, 10(3)
  6. ^Wang, Jinhao, and Michelle Stallone Brown (2007). "Automated Essay Scoring Versus Human Scoring: A Comparative Study", p. 6. Journal of Technology, Learning, and Assessment, 6(2)
  7. ^Bennett, Randy Elliot, and Anat Ben-Simon (2005). Toward Theoretically Meaningful Automated Essay ScoringArchived October 7, 2007, at the Wayback Machine., p. 6. Retrieved 2012-03-19.
  8. ^Page, E.B. (1966). "The imminence of grading essays by computers". Phi Delta Kappan, 47, 238-243.
  9. ^Page, E.B. (1968). "The Use of the Computer in Analyzing Student Essays". International Review of Education, 14(3), 253-263.
  10. ^Page, E.B. (2003), pp. 44-45.
  11. ^MacDonald, N.H., L.T. Frase, P.S. Gingrich, and S.A. Keenan (1982). "The Writers Workbench: Computer Aids for Text Analysis". IEEE Transactions on Communications, 3(1), 105-110.
  12. ^ abPage, E.B. (1994). "New Computer Grading of Student Prose, Using Modern Concepts and Software". Journal of Experimental Education, 62(2), 127-142.
  13. ^Rudner, Lawrence. "Three prominent writing assessment programsArchived March 9, 2012, at the Wayback Machine.". Retrieved 2012-03-06.
  14. ^ abElliot, Scott (2003). "Intellimetric TM: From Here to Validity", p. 75. In: Automated Essay Scoring: A Cross-Disciplinary Perspective. Shermis, Mark D., and Jill Burstein, eds. Lawrence Erlbaum Associates, Mahwah, New Jersey, ISBN 0805839739
  15. ^"IntelliMetric®: How it Works". Retrieved 2012-02-28.
  16. ^Burstein, Jill (2003). "The E-rater(R) Scoring Engine: Automated Essay Scoring with Natural Language Processing", p. 113. In: Automated Essay Scoring: A Cross-Disciplinary Perspective. Shermis, Mark D., and Jill Burstein, eds. Lawrence Erlbaum Associates, Mahwah, New Jersey, ISBN 0805839739
  17. ^ abRudner, Lawrence (ca. 2002). "Computer Grading using Bayesian Networks-OverviewArchived March 8, 2012, at the Wayback Machine.". Retrieved 2012-03-07.
  18. ^"Assessment Technologies", Measurement Incorporated. Retrieved 2012-03-09.
  19. ^"Hewlett prize". Retrieved 2012-03-05.
  20. ^University of Akron (12 April 2012). "Man and machine: Better writers, better grades". Retrieved 4 July 2015. 
  21. ^Shermis, Mark D., and Jill Burstein, eds. Handbook of Automated Essay Evaluation: Current Applications and New Directions. Routledge, 2013.
  22. ^Rivard, Ry (March 15, 2013). "Humans Fight Over Robo-Readers". Inside Higher Ed. Retrieved 14 June 2015. 
  23. ^ abPerelman, Les (August 2013). "Critique of Mark D. Shermis & Ben Hamner, "Contrasting State-of-the-Art Automated Scoring of Essays: Analysis"". Journal of Writing Assessment. 6 (1). Retrieved June 13, 2015. 
  24. ^Perelman, L. (2014). "When 'the state of the art is counting words', Assessing Writing, 21, 104-111.
  25. ^Bennett, Randy E. (March 2015). "The Changing Nature of Educational Assessment". Review of Research in Education. 39 (1): 370–407. 
  26. ^Keith, Timothy Z. (2003), p. 149.
  27. ^Persing, Isaac, and Vincent Ng (2015). "Modeling Argument Strength in Student Essays", pp. 543-552. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Retrieved 2015-10-22.
  28. ^Bennett, Randy Elliot, and Anat Ben-Simon (2005), p. 7.
  29. ^Chung, Gregory K.W.K., and Eva L. Baker (2003). "Issues in the Reliability and Validity of Automated Scoring of Constructed Responses", p. 23. In: Automated Essay Scoring: A Cross-Disciplinary Perspective. Shermis, Mark D., and Jill Burstein, eds. Lawrence Erlbaum Associates, Mahwah, New Jersey, ISBN 0805839739
  30. ^Elliot, Scott (2003), p. 77.
  31. ^Burstein, Jill (2003), p. 114.
  32. ^Bennett, Randy E. (May 2006). "Technology and Writing Assessment: Lessons Learned from the US National Assessment of Educational Progress"(PDF). International Association for Educational Assessment. Retrieved 5 July 2015. 
  33. ^McCurry, D. (2010). "Can machine scoring deal with broad and open writing tests as well as human readers?". Assessing Writing. 15: 118–129. 
  34. ^R. Bridgeman (2013). Shermis, Mark D.; Burstein, Jill, eds. Handbook of Automated Essay Evaluation. New York: Routledge. pp. 221–232. 
  35. ^ abYang, Yongwei, Chad W. Buckendahl, Piotr J. Juszkiewicz, and Dennison S. Bhola (2002). "A Review of Strategies for Validating Computer-Automated ScoringArchived January 13, 2016, at the Wayback Machine.". Applied Measurement in Education, 15(4). Retrieved 2012-03-08.
  36. ^Wang, Jinhao, and Michelle Stallone Brown (2007), pp. 4-5.
  37. ^Dikli, Semire (2006). "An Overview of Automated Scoring of Essays". Journal of Technology, Learning, and Assessment, 5(1)
  38. ^Ben-Simon, Anat (2007). "Introduction to Automated Essay Scoring (AES)". PowerPoint presentation, Tbilisi, Georgia, September 2007.
  39. ^Winerip, Michael (22 April 2012). "Facing a Robo-Grader? Just Keep Obfuscating Mellifluously". The New York Times. Retrieved 5 April 2013. 
  40. ^"Signatures >> Professionals Against Machine Scoring Of Student Essays In High-Stakes Assessment". HumanReaders.Org. Retrieved 5 April 2013. 
  41. ^Markoff, John (4 April 2013). "Essay-Grading Software Offers Professors a Break". The New York Times. Retrieved 5 April 2013. 
  42. ^Larson, Leslie (5 April 2013). "Outrage over software that automatically grades college essays to spare professors from having to assess students'". Daily Mail. Retrieved 5 April 2013. 
  43. ^Garner, Richard (5 April 2013). "Professors angry over essays marked by computer". The Independent. Retrieved 5 April 2013. 
  44. ^Corrigan, Paul T. (25 March 2013). "Petition Against Machine Scoring Essays, HumanReaders.Org". Teaching & Learning in Higher Ed. Retrieved 5 April 2013. 
  45. ^Jaffee, Robert David (5 April 2013). "Computers Cannot Read, Write or Grade Papers". Huffington Post. Retrieved 5 April 2013. 
  46. ^"Professionals Against Machine Scoring Of Student Essays In High-Stakes Assessment". HumanReaders.Org. Retrieved 5 April 2013. 
  47. ^"Research Findings >> Professionals Against Machine Scoring Of Student Essays In High-Stakes Assessment". HumanReaders.Org. Retrieved 5 April 2013. 
  48. ^"Works Cited >> Professionals Against Machine Scoring Of Student Essays In High-Stakes Assessment". HumanReaders.Org. Retrieved 5 April 2013. 
  49. ^"Assessment Technologies." Measurement, Inc.

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