SlowGuess/ACL_2020_OCR huggingface dataset:
The educational measurement community has long been studying fairness in automated scoring (Williamson et al., 2012; Ramineni and Williamson, 2013; AERA, 2014) and recent progress made by the NLP community towards enhancing the usual accuracy-based evaluations with some of these psychometric analyses – from computing indicators of potential biases in automatic scores across various demographic sub-groups to computing new metrics that incorporate measurement theory to produce more reliable indicators of system performance – is quite promising (Madnani et al., 2017b; Loukina et al., 2019).
3.4 Pervasiveness of Technology
Page's gedankenexperiment on the potential of automated essay evaluation in a classroom context no doubt appeared audacious in 1966 but nothing back then could have prepared his readers to the pervasiveness of technology we are experiencing today. Today you can very literally carry your AWE system in your pocket; you can even carry several. You can use them (almost) at any time and at any place – not only in classrooms, but at home, at work, and even while texting with a friend.
This is perhaps the biggest issue that Page's vision did not address: the possibility of universal availability and the concomitant co-optation of a tool beyond its original intended purpose. Much like the calculator – invented by Blaise Pascal to help his father with the tedious arithmetic of tax collection – ended up “freeing” people from the burden of figuring out their intended tip at a restaurant through mental arithmetic, a future writing aid meant to help a student improve his argument writing assignment for a class could end up being used by a lawyer for composing his closing argument. Since such usages are on the horizon, we should consider the implications now.
17https://facctconference.org/
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Once an invention is out in the open, it is difficult to predict what specific uses people would put it to. How do we go about evaluating the tool if we don't know what the user's goal is? While it isn't possible to anticipate all specific uses, it is possible, we believe, to consider the types of uses that suggest different evaluation strategies. From the current vantage point, we see three types of uses.
4.1 Support Consequential Decision Making
The first use is where a consequential decision about the writer or a related entity (such as a class or a school) is being made based on the written product. This use is exemplified by the application of automated scoring in a standardized testing context to decide on admissions to an institution of higher education or the granting of a professional licenses; other cases such as course placement decisions, coursework grading, or even extension of a job offer (where the submission of a writing sample is a part of the job application process) would belong to this type of use. In all such cases, the automated system needs to provide valid and fair scores (or other types of feedback), since the livelihood or professional trajectory of people might depend on the outcome. We have dealt with the particulars of this case in detail in  $\S 3.2$ .
4.2 Create a Better Written Product
The second type of use is one where the focus is on the final product, namely, the actual piece of writing produced following the writer's use of AWE technology. In this context, it does not much matter exactly what part of the final product is due to the human and which part is due to the machine – perhaps the machine only corrected misspellings, or suggested improvements for the human to vet, or maybe the human only contributed the very first ideation, and the machine has done the rest. Perhaps all the human writer contributed was the thesis ('I think school should start at 8 rather than 7') and then clicked 'submit' to get back an essay making a cogent and convincing case in support of the thesis. Mining large textual databases for arguments and evaluating them are feasible today as recently demonstrated by IBM's Debater technology $^{18}$  (Rinott et al., 2015; Levy et al., 2017; Gretz et al., 2019); introduce some figuration to
make it more appealing (Veale et al., 2017; Veale, 2018) and storify it (Riegl and Veale, 2018; Radford et al., 2019), et voilà!
This type of use is essentially a machine's augmentation of human ability, and is hinted at, for example, in a customer testimonial for Grammarly: "Grammarly allows me to get those communications out and feel confident that I'm putting my best foot forward. Grammarly is like a little superpower, especially when I need to be at  $110\%$ ." The human presumably remains at the same level of ability, but the product of the machine-human collaboration is superior to what the human alone could have produced.
In this context, the primary evaluation criterion for AWE is the fitness of the resulting communication to its purpose, or, at least, some evidence of improvement of the product over the human's first draft. Indeed, measurements of improvement across drafts and evidence of students' making corrections following feedback are often used for evaluation (Attali, 2004; Lipnevich and Smith, 2008; Foltz et al., 2014; Chapelle et al., 2015).
Within the product-centered evaluation paradigm, there could be various specific objectives other than the improvement of the holistic quality of the piece of writing; it could be an increase in the speed of production, or the maximization of click-through rate in an advertisement text, for example.
4.3 Help the User Learn to Write Better
The third type of use for AWE software is to help the writer improve his or her writing skill. Scores or other types of feedback are designed, in this context, to provide tutoring or guidance, not for fixing specific problems in the current piece of writing but to help the user learn more general skills that would make the first draft of their next essay better than the first draft of their current essay.
Evaluation of a tool though a demonstration of skill-improvement – the efficacy of the tool – is a complicated endeavor. To demonstrate that the observed improvement in skill is specifically due to the use of the writing tool, and not due to something else happening in students' life and education at the same time requires a research design that can take other potential sources of variation in outcomes into account, such as the one used in randomized controlled studies often used to as-
<sup>18</sup>https://www.research.ibm.com/artificial-intelligence/project-debater/
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sess interventions, including in education (Connolly et al., 2018); some such studies have been performed with respect to AWE tools (Rock, 2007; Wilson and Roscoe, 2020). A tool that allows for monitoring of improvement in skill (even if the improvement is due to other factors such as school instruction or participation in some activity or community) could also be useful in the broader context of skill-oriented use, as the learner and the teacher would be able to tell that improvement is happening, even if we do not know exactly why. Improvement in important aspects of learning such as motivation and self-efficacy could also provide value to the learner (Grimes and Warschauer, 2010; Wilson and Roscoe, 2020).
4.4 Relationships between Types of Use
One could argue that an ideal automated writing assistant would support all the different goals at once - help one produce better writing, help one learn, and do both in a psychometrically responsible fashion - benefits are not restricted to certain types of users more than others - so that decision-making based on the outcome of the usage of the tool can also be supported.
Indeed, the uses are not necessarily mutually exclusive. For example, the human augmentation and consequential decision use cases could apply at the same time. It is possible that, at some future point in time, spelling will be deemed to lie outside of the construct targeted by the consequential assessment of writing and spell-correction software will be made available to test-takers. However, this would require a careful examination of the impact of correction on the distributions and interpretations of the scores. In particular, Choi and Cho (2018) found that manually-vetted correction of spelling errors yielded a significant increase in scores assigned to the essays by trained raters, and that, even after controlling for the error quantity and quality predictors, the magnitude of the average gain in the score was smaller for responses with higher original scores. Add to the mix the finding that automated spelling correction system is more accurate on essays that are of better quality to begin with (Flor, 2012), and it's likely that the automated assessment of an automatically spell-corrected version of an essay might show an unexpected relationship with original scores that would need to be closely examined for bias or for an increase in construct-irrelevant variance.
It is also possible that the effect of using a tool optimized for one use case could be the opposite of what another use case requires. If 'use it or lose it' has any truth to it, a potential consequence of extensive, consistent, and pervasive human augmentation for producing superior written products is an adverse impact on the skill of the human in the human-machine team. If the near universal adoption of calculators is any guide, once a skill (long division) can be reliably outsourced to a machine, humans stop valuing it in daily practice and, therefore, might set out to lose it in the long run.[19] Spelling is a likely candidate writing skill where reliable access to high quality correction software could make humans stop worrying about it rather than invest effort in improving it.
Many of the tools mentioned in §2.2.4 seem to position themselves somewhere between the skill-improvement and the product-improvement use cases, perhaps assuming that quantity will eventually turn into quality, namely, extensive work on improving the written product might lead to internalization and generalization of the skill to new contexts. This might or might not be true. Feedback that helps the user fix an error quickly by pointing it out and by suggesting a correction might be good in a product-oriented context, but not in a skill-oriented context; letting the user pinpoint and fix the error himself or herself might be a better skill-development strategy (Hyland and Hyland, 2006). According to Graham and Perin (2007) meta-analysis of writing interventions for adolescents, explicit grammar instruction tended to be ineffective; this finding is cited by the developers for Writing Pal to support their decision to forgo giving explicit feedback on grammar (McNamara et al., 2013), in contrast to most other AWE systems that do provide such feedback.
5 Summary & Conclusion
In his visionary paper from 1966, Ellis Page provided a proof-of-concept demonstration of the possibility of automated grading of essays, as well
199 Curriculum and Evaluation Standards for School Mathematics from the National Council of Teachers of Mathematics recommend in the Summary of Changes to Content and Emphasis in K-4 Mathematics (p.21) decreasing the attention devoted to long division specifically and to "complex paper-and-pencil computations" in general; the recommendation for grades 5-8 is likewise to decrease emphasis on "tedious paper-and-pencil computations" (p.71). https://archive.org/details/curriculumvaluea00nati. The document has sparked substantial controversy, including with regards to long division (Klein and Milgram, 2000).
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as outlined some potential challenges to its adoption. Subsequent research and practice have delivered on Page's minimum desiderata for an AWE system; current research is working to address the outstanding challenges dealing with a variety of languages, content domains, and writing tasks.
The field of AWE has thus progressed according to the trajectory charted by Page to a large extent, though not completely. In particular, while Page imagined the main use case of AWE to be in the service of a harried English teacher and his feedback-thirsty students, in reality, the most visible use case has arguably evolved to be automated scoring of essays for standardized testing, which, in turn, has led to new challenges, such as ensuring the validity and fairness of scores.
The other development that Page could not anticipate is the sheer pervasiveness of technology in people's daily lives; AWE software can be made available not only in classrooms to be used under the watchful eye of the English teacher, but (almost) anywhere and at any time, including on mobile devices. While it is difficult to predict specific uses people would find for such software, we outlined a number of types of use, depending on the goal: (a) consequential decision making about the user; (b) delivery of the best possible written product in partnership with the user; and (c) assisting the user in improving her writing skills. We believe that we, as researchers, can help users find value in our technology by considering the goals, engaging partners from other relevant disciplines, and designing the tools as well as their evaluations to focus on specific types of use.
We would like to thank our colleagues Anastassia Loukina, Jill Burstein, Aoife Cahill, and Isaac Bejar, as well as ACL reviewers and area chair, for their thoughtful comments on earlier drafts of this paper.
AERA. 2014. Standards for Educational and Psychological Testing. American Educational Research Association.
Dimitrios Alikaniotis, Helen Yannakoudakis, and Marek Rei. 2016. Automatic text scoring using neural networks. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 715-725.
Laura Allen, Scott Crossley, Erica Snow, and Danielle McNamara. 2014. L2 writing practice: Game enjoyment as a key to engagement. *Language Learning & Technology*, 18(2):124-150.
Evelin Amorim and Adriano Veloso. 2017. A multi-aspect analysis of automatic essay scoring for Brazilian Portuguese. In Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics, pages 94-102.
Yigal Attali. 2004. Exploring the feedback and revision features of criterion. Journal of Second Language Writing, 14:191-205.
Yigal Attali and Jill Burstein. 2006. Automated Essay Scoring with e-ratero V. 2. The Journal of Technology, Learning and Assessment, 4(3):1-30.
Lifang Bai and Guangwei Hu. 2017. In the face of fallible awe feedback: how do students respond? Educational Psychology, 37(1):67-81.
Sumit Basu, Chuck Jacobs, and Lucy Vanderwende. 2013. Powergrading: a Clustering Aapproach to Amplify Human Effort for Short Answer Grading. Transactions of the Association for Computational Linguistics, 1:391-402.
Beata Beigman Klebanov, Jill Burstein, Judith Harackiewicz, Stacy Priniski, and Matthew Mulholland. 2016a. Enhancing STEM motivation through personal and communal values: NLP for assessment of utility value in student writing. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, pages 199-205.
Beata Beigman Klebanov and Michael Flor. 2013a. Argumentation-relevant metaphors in test-taker essays. In Proceedings of the First Workshop on Metaphor in NLP, pages 11-20.
Beata Beigman Klebanov and Michael Flor. 2013b. Word association profiles and their use for automated scoring of essays. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1148-1158.
Beata Beigman Klebanov, Michael Flor, and Binod Gyawali. 2016b. Topicality-Based Indices for Essay Scoring. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, pages 63-72.
Beata Beigman Klebanov, Binod Gyawali, and Yi Song. 2017. Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron? In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 244-249.
Beata Beigman Klebanov, Chee Wee (Ben) Leong, and Michael Flor. 2018. A corpus of non-native written English annotated for metaphor. In Proceedings of the 2018 Conference of the North American
7804
Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 86-91.
Beata Beigman Klebanov, Nitin Madnani, Jill Burstein, and Swapna Somasundaran. 2014. Content importance models for scoring writing from sources. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 247-252.
Isaac Bejar, Michael Flor, Yoko Futagi, and Chaintanya Ramineni. 2014. On the vulnerability of automated scoring to construct-irrelevant response strategies (cirs): An illustration. Assessing Writing, 22:48-59.
Isaac Bejar, Waverely VanWinkle, Nitin Madnani, William Lewis, and Michael Steier. 2013. Length of textual response as a construct-irrelevant response strategy: The case of shell language. ETS Research Report Series, 2013(1):1-39.
Randy Elliot Bennett and Isaac I Bejar. 1998. Validity and automad scoring: It's not only the scoring. Educational Measurement: Issues and Practice, 17(4):9-17.
Stig Johan Berggren, Taraka Rama, and Lilja Øvrelid. 2019. Regression or classification? automated essay scoring for Norwegian. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 92-102.
Kay Berkling. 2018. A 2nd longitudinal corpus for childrens writing with enhanced output for specific spelling patterns. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
Steven Burrows, Iryna Gurevych, and Benno Stein. 2015. The Eras and Trends of Automatic Short Answer Grading. International Journal of Artificial Intelligence in Education, 25(1):60-117.
Jill Burstein, Martin Chodorow, and Claudia Leacock. 2004. Automated essay evaluation: The criterion online writing service. AI Magazine, 25(3):27-36.
Jill Burstein, Norbert Elliot, Beata Beigman Klebanov, Nitin Madnani, Diane Napolitano, Max Schwartz, Patrick Houghton, and Hilary Molloy. 2018. Writing mentor: Writing progress using self-regulated writing support. Journal of Writing Analytics, 2:314-328.
Jill Burstein, Daniel Marcu, and Kevin Knight. 2003. Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays. IEEE Intelligent Systems, 18(1):32-39.
Jill Burstein, Joel Tetreault, and Slava Andreyev. 2010. Using Entity-Based Features to Model Coherence in Student Essays. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 681-684.
Aoife Cahill, Martin Chodorow, and Michael Flor. 2018. Developing an e-rater Advisory to Detect Babel-generated Essays. Journal of Writing Analytics, 2:203-224.
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183-186.
Winston Carlile, Nishant Gurrapadi, Zixuan Ke, and Vincent Ng. 2018. Give me more feedback: Annotating argument persuasiveness and related attributes in student essays. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 621-631.
Carol Chapelle, Elena Cotos, and Jooyoung Lee. 2015. Validity arguments for diagnostic assessment using automated writing evaluation. Language Testing, 32(3):385-405.
Hongbo Chen and Ben He. 2013. Automated Essay Scoring by Maximizing Human-Machine Agreement. In Proceedings of EMNLP, pages 1741-1752.
Ikkyu Choi and Yeonsuk Cho. 2018. The impact of spelling errors on trained raters' scoring decisions. Language Education & Assessment, 1(2):45-58.
Jaeho Choi. 2010. The Impact of Automated Essay Scoring (AES) for Improving English Language Learner's Essay Writing. University of Virginia Charlottesville, VA.
Brian E. Clauser, Michael T. Kane, and David B. Swanson. 2002. Validity issues for performance-based tests scored with computer-automated scoring systems. Applied Measurement in Education, 15(4):413-432.
Paul Connolly, Ciara Keenan, and Karolina Urbanska. 2018. The trials of evidence-based practice in education: A systematic review of randomised controlled trials in education research 1980-2016. Educational Research.
Ronan Cummins, Meng Zhang, and Ted Briscoe. 2016. Constrained Multi-Task Learning for Automated Essay Scoring. In Proceedings of ACL, pages 789-799.
Tirthankar Dasgupta, Abir Naskar, Lipika Dey, and Rupsa Saha. 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, pages 93-102.
Paul Deane. 2013. On the Relation between Automated Essay Scoring and Modern Views of the Writing Construct. Assessing Writing, 18(1):7-24.
Paul Diederich, John French, and Sydell Carlton. 1961. Factors in judgments of writing ability. ETS Research Bulletin, RB-61-15.
Fei Dong, Yue Zhang, and Jie Yang. 2017. Attention-based recurrent convolutional neural network for automatic essay scoring. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 153-162.
Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. CoRR, abs/1702.08608.
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS 12, page 214226.
Carol Englert, Yong Zhao, Kailonnie Dunsmore, Natalia Yevgenyevna Collings, and Kimberly Wolbers. 2007. Scaffolding the writing of students with disabilities through procedural facilitation: Using an internet-based technology to improve performance. Learning Disability Quarterly, 30(1):9-29.
Michael Flor. 2012. Four types of context for automatic spelling correction. Traitément Automatique des Langues (TAL), 53(3):61-99.
Michael Flor and Aoife Cahill. 2020. Automated scoring of open-ended written responses - possibilities and challenges. In Innovative Computer-based International Large-Scale Assessments. Springer Science Publishers.
Michael Flor and Yoko Futagi. 2013. Producing an annotated corpus with automatic spelling correction. In S. Granger, G. Gilquin, and F. Meunier, editors, Twenty Years of Learner Corpus Research: Looking back, moving ahead, pages 139-154. Louvain-la-Neuve: Presses universitaires de Louvain.
Peter Foltz, Mark Rosenstein, Nicholas Dronen, and Scott Dooley. 2014. Automated feedback in a large-scale implementation of a formative writing system: Implications for improving student writing. In *Paper presented at the annual meeting of the American Educational Research Association*, Philadelphia, PA.
Josh Gardner, Christopher Brooks, and Ryan Baker. 2019. Evaluating the fairness of predictive student models through slicing analysis. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge, LAK19, page 225234.
Debanjan Ghosh, Aquila Khanam, Yubo Han, and Smaranda Muresan. 2016. Coarse-grained argumentation features for scoring persuasive essays. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 549-554.
Steve Graham and Dolores Perin. 2007. A meta-analysis of writing instruction for adolescent students. Journal of educational psychology, 99(3):445.