Equity Beyond Bias in Language Technologies for Education
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Elijah Mayfield, Michael Madaio, Shrimai Prabhumoye, David Gerritsen, Brittany McLaughlin, Ezekiel Dixon-Román, Alan W Black. Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications. 2019.
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There is no such thing as neutral, dispassionate non-human assessment.
“There is no such thing as neutral, dispassionate non-human assessment (Mayfield et al., 2019;Scheuneman, 1979).”
From Assessment in the age of artificial intelligence (2022) · cites this paper as Mayfield et al., 2019
Section 5.2 says bias can enter automated writing assessment through rubrics and training-data scoring, supporting the point that non-human assessment is not automatically neutral. But it also allows that extensive countermeasures can address bias; it does not establish the categorical claim that neutral non-human assessment cannot exist. The indexed passages provide section labels but no page numbers. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAIEd systems' impacts result from a complex interplay between the AI systems themselves, the human teams who design and develop them, and the human decision makers who use them.
“their impacts result from a complex interplay between the AI systems themselves, the human teams who design and develop them, and the human decision makers who use these systems”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as Mayfield et al., 2019
Section 8.1 links system impacts to the cultural competence and diversity of development teams, while Section 4.1 describes how design choices affect learners. The Introduction says these tools are used in educational decision-making, but the passages do not establish how the human decision makers who use them shape their impacts. Our copy supplies no page numbers for these sections. (LLM, conf=0.79)
Experimental — accuracy not yet measuredInsofar as AIEd technologies are shaped or driven by historical data, they risk perpetuating or even amplifying any social inequities reflected in these data.
“insofar as these technologies are shaped or driven by historical data, they risk perpetuating or even amplifying any social inequities reflected in these data (Mayfield et al., 2019; Selbst et al., 2019; V eale et al., 2018).”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as Mayfield et al., 2019
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Cites
Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color
Crenshaw, K. (1991) · Stanford Law Review · doi:10.2307/1229039 · Cited by 29,585 · see citing works
Toward a Theory of Culturally Relevant Pedagogy
Ladson-Billings, G. (1995) · American Educational Research Journal · doi:10.3102/00028312032003465 · Cited by 8,057 · see citing works
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405 · see citing works
Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms
Moll, L. C., Amanti, C., Neff, D., & Gonzalez, N. (1992) · Theory Into Practice · doi:10.1080/00405849209543534 · Cited by 7,339 · see citing works
Meaningful Differences in the Everyday Experience of Young American Children
Panagos, R. J. (1998) · Remedial and Special Education · doi:10.1177/074193259801900609 · Cited by 4,183 · see citing works
Meaningful Differences in the Everyday Experience of Young American Children
(1996) · Choice Reviews Online · doi:10.5860/choice.33-3435 · Cited by 4,103 · see citing works
Fishman, J. A., & Phillipson, R. (1993) · Modern Language Journal · doi:10.2307/329140 · Cited by 3,758 · see citing works
(2018) · Cited by 3,510 · see citing works
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 3,432 · cited 1× · see citing works
“Some have questioned the value of fairness research, arguing that machine learning may simply reproduce existing distributions, rather than cause harm in itself (see Mittelstadt et al. (2016) for an overview of this debate).”
BackgroundBlack students' school success: Coping with the ?burden of ?acting white??
Fordham, S., & Ogbu, J. U. (1986) · The Urban Review · doi:10.1007/bf01112192 · Cited by 3,323 · see citing works
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Lebovits, H. (2018) · Public Integrity · doi:10.1080/10999922.2018.1511671 · Cited by 3,318 · see citing works
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works
Big other: Surveillance Capitalism and the Prospects of an Information Civilization
Zuboff, S. (2015) · Journal of Information Technology · doi:10.1057/jit.2015.5 · Cited by 2,978 · cited 1× · see citing works
“If we accept the premise that dominance hierarchies play a key role in education, it follows to acknowledge large-scale edtech that tracks students’ activity in real time as one instantiation of “surveillance capitalism” in schools (Zuboff, 2015).”
BackgroundSemantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887 · see citing works
Algorithms of Oppression: How Search Engines Reinforce Racism
Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709 · see citing works
Taylor, R. D., Oberle, E., Durlak, J. A., & Weissberg, R. P. (2017) · Child Development · doi:10.1111/cdev.12864 · Cited by 2,345 · cited 1× · see citing works
“This is important for education, both for design of affect-oriented curriculum (Taylor et al., 2017) and funding for socioemotional skills (Chan Zuckerberg Initiative, 2018).”
BackgroundFair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
Teaching Community: A Pedagogy of Hope
hooks, B. (2003) · Cited by 2,048 · see citing works
Ananny, M., & Crawford, K. (2018) · New Media & Society · doi:10.1177/1461444816676645 · Cited by 1,836 · see citing works
Fairness and Abstraction in Sociotechnical Systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287598 · Cited by 1,631 · see citing works
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works
The Politics of Education: Culture, Power and Liberation
Bruno-Jofre, R. D. C., Freiré, P., & Macedo, D. P. (1986) · Canadian Journal of Education / Revue canadienne de l éducation · doi:10.2307/1494617 · Cited by 1,396 · see citing works
Communities of Practice and Social Learning Systems: the Career of a Concept
Wenger, E. (2010) · Social Learning Systems and Communities of Practice · doi:10.1007/978-1-84996-133-2_11 · Cited by 1,348 · see citing works
Educational Data Mining and Learning Analytics
Baker, R. S., & Inventado, P. S. (2014) · Springer eBooks · doi:10.1007/978-1-4614-3305-7_4 · Cited by 1,333 · see citing works
The Whale and the Reactor: A Search for Limits in an Age of High Technology
McGucken, W., & Winner, L. (1988) · Technology and Culture · doi:10.2307/3105261 · Cited by 1,264 · see citing works
(2018) · Cited by 1,167 · see citing works
Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098095 · Cited by 1,149 · see citing works
Dynamics of affective states during complex learning
D’Mello, S. K., & Graesser, A. C. (2011) · Learning and Instruction · doi:10.1016/j.learninstruc.2011.10.001 · Cited by 1,101 · see citing works
Word embeddings quantify 100 years of gender and ethnic stereotypes
Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720347115 · Cited by 1,065 · see citing works
Vygotsky and Education: Instructional Implications and Applications of Sociohistorical Psychology
Mathews, P. V., & Moll, L. C. (1991) · British Journal of Educational Studies · doi:10.2307/3120999 · Cited by 1,059 · see citing works
The Zone of Proximal Development in Vygotsky's Analysis of Learning and Instruction
Chaiklin, S. (2003) · Vygotsky's Educational Theory in Cultural Context · doi:10.1017/cbo9780511840975.004 · Cited by 1,055 · see citing works
Winner, L. (1988) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226902098.001.0001 · Cited by 1,012 · see citing works
A review of rubric use in higher education
Reddy, Y. M., & Andrade, H. (2010) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930902862859 · Cited by 917 · see citing works
Education for Extinction: American Indians and the Boarding School Experience, 1875-1928
Szasz, M., & Adams, D. (1996) · History of Education Quarterly · doi:10.2307/369797 · Cited by 834 · see citing works
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · cited 1× · see citing works
“Early work on “de-biasing” NLP has begun, seeking to reduce the amplifica-tion of bias in dense word embeddings (Boluk-basi et al., 2016; Zhao et al., 2017, 2018b); but early results still leave room for improvement (Go-nen and Goldberg, 2019).”
BackgroundCulturally Sustaining Pedagogies: Teaching and Learning for Justice in a Changing World
Peristeris, K. (2017) · Journal of Teaching and Learning · doi:10.22329/jtl.v11i1.4987 · Cited by 816 · see citing works
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
Gimpel, K., Schneider, N., O'Connor, B., Das, D., Mills, D. P., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473609.v1 · Cited by 800 · see citing works
Education for Extinction: American Indians and the Boarding School Experience, 1875-1928
Vaughn, C., & Adams, D. W. (1996) · Western Historical Quarterly · doi:10.2307/970154 · Cited by 720 · see citing works
(2016) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780190625696.001.0001 · Cited by 705 · see citing works
Understanding the consequences of bilingualism for language processing and cognition
Kroll, J. F., & Bialystok, E. (2013) · Journal of Cognitive Psychology · doi:10.1080/20445911.2013.799170 · Cited by 696 · see citing works
Gender bias in coreference resolution: Evaluation and debiasing methods
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2003 · Cited by 684 · see citing works
Fairness in Machine Learning: Lessons from Political Philosophy
Binns, R. (2017) · arXiv · doi:10.48550/arxiv.1712.03586 · Cited by 676 · see citing works
Cognitive Consequences of Formal and Informal Education
Scribner, S., & Cole, M. (1973) · Science · doi:10.1126/science.182.4112.553 · Cited by 635 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
Privacy in the Internet of Things: threats and challenges
Ziegeldorf, J. H., Morchon, O. G., & Wehrle, K. (2013) · Security and Communication Networks · doi:10.1002/sec.795 · Cited by 625 · see citing works
Culturally Sustaining Pedagogies: Teaching and Learning for Justice in a Changing World
Paris, D., & Alim, H. S. (2017) · Cited by 617 · see citing works
A comparative study of fairness-enhancing interventions in machine learning
Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., & Roth, D. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287589 · Cited by 601 · see citing works
The CoNLL-2014 Shared Task on Grammatical Error Correction
Ng, H. T., Wu, S. M., Briscoe, T., Hadiwinoto, C., Susanto, R. H., & Bryant, C. (2014) · doi:10.3115/v1/w14-1701 · Cited by 578 · see citing works
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1198 · Cited by 572 · see citing works
The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition
Keyes, O. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274357 · Cited by 505 · see citing works
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
Gimpel, K., Schneider, N., O'Connor, B., Das, D., Mills, D. S., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., & Smith, N. A. (2010) · doi:10.21236/ada547371 · Cited by 491 · see citing works
Analysis Methods in Neural Language Processing: A Survey
Belinkov, Y., & Glass, J. R. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00254 · Cited by 485 · see citing works
Mapping the Early Language Environment Using All-Day Recordings and Automated Analysis
Gilkerson, J., Richards, J. A., Warren, S. F., Montgomery, J. K., Greenwood, C. R., Kimbrough Oller, D., Hansen, J. H. L., & Paul, T. D. (2017) · American Journal of Speech-Language Pathology · doi:10.1044/2016_ajslp-15-0169 · Cited by 472 · see citing works
The Measure and Mismeasure of Fairness
Corbett-Davies, S., Gaebler, J. D., Nilforoshan, H., Shroff, R., & Goel, S. (2018) · arXiv · doi:10.48550/arxiv.1808.00023 · Cited by 444 · see citing works
Research-Based Design of Pedagogical Agent Roles: a Review, Progress, and Recommendations
Kim, Y. A., & Baylor, A. L. (2015) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0055-y · Cited by 423 · see citing works
Language from police body camera footage shows racial disparities in officer respect
Voigt, R., Camp, N. P., Prabhakaran, V., Hamilton, W. L., Hetey, R. C., Griffiths, C. M., Jurgens, D., Jurafsky, D., & Eberhardt, J. L. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1702413114 · Cited by 407 · see citing works
Critical Literacy and Urban Youth: Pedagogies of Access, Dissent, and Liberation
Morrell, E. (2007) · Cited by 403 · see citing works
Toward an Ethics of Algorithms: Convening, Observation, Probability, and Timeliness
Ananny, M. (2016) · Science, Technology, & Human Values · doi:10.1177/0162243915606523 · Cited by 395 · see citing works
Reexamining the Verbal Environments of Children From Different Socioeconomic Backgrounds
Sperry, D. E., Sperry, L. L., & Miller, P. J. (2018) · Child Development · doi:10.1111/cdev.13072 · Cited by 385 · see citing works
The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing
Dror, R., Baumer, G., Shlomov, S., & Reichart, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1128 · Cited by 384 · see citing works
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Kiritchenko, S., & Mohammad, S. (2018) · Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s18-2005 · Cited by 384 · see citing works
Learning Gender-Neutral Word Embeddings
Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K.-W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1521 · Cited by 376 · see citing works
Affect-aware tutors: recognising and responding to student affect
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009) · International Journal of Learning Technology · doi:10.1504/ijlt.2009.028804 · Cited by 371 · see citing works
Problems in Current Text Simplification Research: New Data Can Help
Xu, W. H., Callison-Burch, C., & Napoles, C. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00139 · Cited by 369 · see citing works
Good Question! Statistical Ranking for Question Generation
Heilman, M., & Smith, N. A. (2018) · Figshare · doi:10.1184/r1/6473390 · Cited by 366 · see citing works
The social impact of natural language processing
Hovy, D., & Spruit, S. L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2096 · Cited by 329 · see citing works
What you can cram into a single vector: Probing sentence embeddings for linguistic propertiesPreprint
Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01070 · Cited by 315 · see citing works
Raciolinguistics: How Language Shapes Our Ideas About Race
Leung, C. (2019) · TESOL Quarterly · doi:10.1002/tesq.554 · Cited by 313 · see citing works
Does String-Based Neural MT Learn Source Syntax?
Shi, X., Padhi, I., & Knight, K. K. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1159 · Cited by 302 · see citing works
Climate Change Skepticism and Denial
Dunlap, R. E. (2013) · American Behavioral Scientist · doi:10.1177/0002764213477097 · Cited by 296 · see citing works
Demographic dialectal variation in social media: a case study of African-American English
Blodgett, S. L., Green, L., & O'Connor, B. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1120 · Cited by 291 · cited 2× · see citing works
“The relative sparsity of data for these variations may have resulted in unacceptable modeling accuracy in the past (Blodgett et al., 2016), but we are now closing that gap (Dalmia et al., 2018; Sitaram et al., 2019).”
BackgroundLevy, M., & Stockwell, G. (2013) · Routledge eBooks · doi:10.4324/9780203708200 · Cited by 287 · see citing works
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring
Nye, B. D., Graesser, A. C., & Hu, X. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-014-0029-5 · Cited by 286 · see citing works
Regan, P. M., & Jesse, J. (2019) · Ethics and Information Technology · doi:10.1007/s10676-018-9492-2 · Cited by 286 · see citing works
Sentiment Analysis in MOOC Discussion Forums: What does it tell us?
Wen, M., Yang, D., & Rosé, C. P. (2014) · Cited by 285 · see citing works
Petitto, L. A., Berens, M. S., Kovelman, I., Dubins, M. H., Jasinska, K., & Shalinsky, M. (2012) · Brain and Language · doi:10.1016/j.bandl.2011.05.003 · Cited by 285 · see citing works
An Analysis of Encoder Representations in Transformer-Based Machine Translation
Raganato, A., & Tiedemann, J. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5431 · Cited by 277 · see citing works
Contemporary Computer-Assisted Language Learning
Thomas, M., Reinders, H., & Warschauer, M. (2012) · Cited by 260 · see citing works
An overview of spoken language technology for education
Eskénazi, M. (2009) · Speech Communication · doi:10.1016/j.specom.2009.04.005 · Cited by 257 · see citing works
Why Students Engage in “Gaming the System” Behavior in Interactive Learning Environments
Baker, R. S., Walonoski, J. A., Heffernan, N. T., Roll, I., Corbett, A. T., & Koedinger, K. R. (2008) · Cited by 256 · see citing works
Gonen, H., & Goldberg, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03862 · Cited by 242 · see citing works
Delayed Impact of Fair Machine Learning
Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., & Hardt, M. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/862 · Cited by 236 · see citing works
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks
Kaushik, D., & Lipton, Z. C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1546 · Cited by 229 · see citing works
Invited Forum: Bridging the “Language Gap”
Avineri, N., Johnson, E. J., Heath, S. B., McCarty, T. L., Ochs, E., Kremer‐Sadlik, T., Blum, S. D., Zentella, A. C., Rosa, J., Flores, N., et al. (2015) · Journal of Linguistic Anthropology · doi:10.1111/jola.12071 · Cited by 225 · see citing works
Teacherbot: interventions in automated teaching
Bayne, S. (2015) · Teaching in Higher Education · doi:10.1080/13562517.2015.1020783 · Cited by 215 · see citing works
A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M. B., & De Choudhury, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287587 · Cited by 214 · see citing works
Educational Data Mining and Learning Analytics
Baker, R. S., Martin, T., & Rossi, L. M. (2016) · John Wiley & Sons, Inc. eBooks · doi:10.1002/9781118956588.ch16 · Cited by 213 · see citing works
Vajjala, S., & Meurers, D. (2012) · Cited by 206 · see citing works
Algorithms of Oppression: How Search Engines Reinforce Racism
Fitch, D. (2018) · Journal of Technology in Human Services · doi:10.1080/15228835.2018.1491371 · Cited by 199 · see citing works
Instructional design of interactive multimedia: A cultural critique
Henderson, L. (1996) · Educational Technology Research and Development · doi:10.1007/bf02299823 · Cited by 198 · see citing works
An Empirical Study of Rich Subgroup Fairness for Machine Learning
Kearns, M. J., Neel, S., Roth, A., & Wu, Z. S. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287592 · Cited by 196 · see citing works
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media
Volkova, S., Wilson, T., & Yarowsky, D. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1187 · Cited by 191 · see citing works
Accommodating variation: Dialects, idiolects, and speech processing
Tanya Kraljic, A., Brennan, S. E., & Samuel, A. G. (2007) · Cognition · doi:10.1016/j.cognition.2007.07.013 · Cited by 189 · see citing works
THE CASE FOR USING DUOLINGO AS PART OF THE LANGUAGE CLASSROOM EXPERIENCE
Munday, P. (2015) · RIED Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.19.1.14581 · Cited by 187 · see citing works
Eddy, S. L., Brownell, S. E., Thummaphan, P., Lan, M., & Wenderoth, M. P. (2015) · CBE—Life Sciences Education · doi:10.1187/cbe.15-05-0108 · Cited by 186 · see citing works
Instruction Based on Adaptive Learning Technologies
Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2025) · Routledge eBooks · doi:10.4324/9781003484776-26 · Cited by 184 · see citing works
Finn, A. N., Schrodt, P., Witt, P. L., Elledge, N., Jernberg, K. A., & Larson, L. (2009) · Communication Education · doi:10.1080/03634520903131154 · Cited by 178 · see citing works
Automated Writing Evaluation and Feedback: Multiple Metrics of Efficacy
Wilson, J., & Roscoe, R. D. (2020) · Journal of Educational Computing Research · doi:10.1177/0735633119830764 · Cited by 176 · see citing works
Combining click-stream data with NLP tools to better understand MOOC completion
Crossley, S. A., Paquette, L., Dascălu, M., McNamara, D. S., & Baker, R. S. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883931 · Cited by 166 · see citing works
Ethical Challenges in Data-Driven Dialogue Systems
Henderson, P., Sinha, K., Gontier, N., Ke, N. R., Fried, G., Lowe, R., & Pineau, J. (2018) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3278721.3278777 · Cited by 166 · see citing works
Overview of the 2012 Shared Task on Parsing the Web
Petrov, S., & McDonald, R. (2012) · Cited by 158 · see citing works
Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs
Adamson, D., Dyke, G., Jang, H., & Rosé, C. P. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-013-0012-6 · Cited by 156 · see citing works
Gonen, H., & Goldberg, Y. (2019) · arXiv (Cornell University) · Cited by 156 · see citing works
Form and Ideology: Arabic Sociolinguistics and Beyond
Haeri, N. (2000) · Annual Review of Anthropology · doi:10.1146/annurev.anthro.29.1.61 · Cited by 155 · see citing works
A review of ASR technologies for children's speech
Gerosa, M., Giuliani, D., Narayanan, S. S., & Potamianos, A. (2009) · Workshop on Child, Computer and Interaction (WOCCI) · doi:10.1145/1640377.1640384 · Cited by 143 · see citing works
Tutorial Dialogue as Adaptive Collaborative Learning Support
Kumar, R., Rosé, C. P., Wang, Y., Joshi, M., & Robinson, A. L. (2007) · Cited by 134 · see citing works
Natural Language Processing for Enhancing Teaching and Learning
Litman, D. J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9879 · Cited by 123 · see citing works
Hornberger, N. H., & Vaish, V. (2008) · Compare A Journal of Comparative and International Education · doi:10.1080/03057920802469663 · Cited by 119 · see citing works
The effect of professor ethnicity and gender on student evaluations: Judged before met.
Bavishi, A., Madera, J. M., & Hebl, M. R. (2010) · Journal of Diversity in Higher Education · doi:10.1037/a0020763 · Cited by 118 · see citing works
Readability assessment for text simplification
Aluísio, S. M. (2024) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.57702/julp5d8d · Cited by 116 · see citing works
Incorporating Dialectal Variability for Socially Equitable Language Identification
Jurgens, D., Tsvetkov, Y., & Jurafsky, D. (2017) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p17-2009 · Cited by 108 · see citing works
The racializing forces of/in AI educational technologies
Dixon‐Román, E. J., Nichols, T. P., & Nyame-Mensah, A. (2019) · Learning Media and Technology · doi:10.1080/17439884.2020.1667825 · Cited by 102 · see citing works
Self-citationAğırdağ, O. (2013) · International Journal of Bilingual Education and Bilingualism · doi:10.1080/13670050.2013.816264 · Cited by 101 · see citing works
BEYOND ESSAY LENGTH: EVALUATING E‐RATER®'S PERFORMANCE ON TOEFL® ESSAYS
Chodorow, M. S., & Burstein, J. C. (2004) · ETS Research Report Series · doi:10.1002/j.2333-8504.2004.tb01931.x · Cited by 99 · see citing works
Wong-Villacrés, M., Kumar, A., Vishwanath, A., Karusala, N., DiSalvo, B. J., & Kumar, N. (2018) · Designing Interactive Systems Conference · doi:10.1145/3196709.3196794 · Cited by 99 · see citing works
A Survey of Code-switched Speech and Language ProcessingPreprint
Sitaram, S., Chandu, K. R., Rallabandi, S. K., & Black, A. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.00784 · Cited by 94 · see citing works
Self-citationMostow, J., Aist, G., Burkhead, P., Corbett, A. T., Cuneo, A., Eitelman, S. M., Huang, C., Junker, B. W., Sklar, M. B., & Tobin, B. (2003) · Journal of Educational Computing Research · doi:10.2190/06ax-qw99-eq5g-rdcf · Cited by 94 · see citing works
Dwork, C., & Ilvento, C. (2019) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2019.33 · Cited by 91 · see citing works
Learner corpora and natural language processing
Meurers, D. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781139649414.024 · Cited by 88 · see citing works
White, J. W. (2011) · Journal of Language Identity & Education · doi:10.1080/15348458.2011.598128 · Cited by 85 · see citing works
Argument Mining for Improving the Automated Scoring of Persuasive Essays
Nguyen, H. V., & Litman, D. J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12046 · Cited by 84 · see citing works
The Social Impact Of Natural Language Processing
Hovy, D., & Spruit, S. L. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1000924 · Cited by 80 · see citing works
Sequence-Based Multi-Lingual Low Resource Speech Recognition
Dalmia, S., Sanabria, R., Metze, F., & Black, A. W. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461802 · Cited by 79 · see citing works
Self-citationClassroom Participation and Student-Faculty Interactions: Does Gender Matter?
Tatum, H. E., Schwartz, B. M., Schimmoeller, P., & Perry, N. (2013) · The Journal of Higher Education · doi:10.1080/00221546.2013.11777309 · Cited by 74 · see citing works
Second Language Acquisition Modeling
Settles, B., Brust, C., Gustafson, E., Hagiwara, M., & Madnani, N. (2018) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w18-0506 · Cited by 74 · see citing works
Coarse-grained Argumentation Features for Scoring Persuasive Essays
Ghosh, D., Khanam, A., Han, Y., & Muresan, S. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2089 · Cited by 71 · see citing works
Ethical by Design: Ethics Best Practices for Natural Language Processing
Leidner, J. L., & Plachouras, V. (2017) · doi:10.18653/v1/w17-1604 · Cited by 70 · see citing works
The Politics of Education: Culture, Power and Liberation
Plunkett, D., & Freiré, P. (1986) · British Journal of Educational Studies · doi:10.2307/3121329 · Cited by 70 · see citing works
The Effects of Culturally Congruent Educational Technologies on Student Achievement
Finkelstein, S., Yarzebinski, E., Vaughn, C., Ogan, A., & Cassell, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-39112-5_50 · Cited by 68 · see citing works
End-to-End Neural Network Based Automated Speech Scoring
Chen, L., Tao, J., Ghaffarzadegan, S., & Qian, Y. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462562 · Cited by 67 · see citing works
Subject reactivity in direct observational assessment: A review and critical analysis
Harris, F. C., & Lahey, B. B. (1982) · Clinical Psychology Review · doi:10.1016/0272-7358(82)90028-9 · Cited by 66 · see citing works
Kumar, A., Reddy, P., Tewari, A., Agrawal, R., & Kam, M. (2012) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208564 · Cited by 66 · see citing works
Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets
Dror, R., Baumer, G., Bogomolov, M., & Reichart, R. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00074 · Cited by 63 · see citing works
Teaching Language to Deaf Infants with a Robot and a Virtual Human
Scassellati, B. M., Brawer, J., Tsui, K. M., Gilani, S. N., Malzkuhn, M., Manini, B., Stone, A., Kartheiser, G., Merla, A., Shapiro, A., et al. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174127 · Cited by 62 · see citing works
Challenges of studying and processing dialects in social media
Jørgensen, A., Hovy, D., & Søgaard, A. (2015) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/w15-4302 · Cited by 62 · see citing works
An Urgency of Teachers: the Work of Critical Digital Pedagogy
Morris, S. M., & Stommel, J. (2018) · Bibliothèque et Archives nationales du Québec (Québec government) · Cited by 62 · see citing works
Formative Essay Feedback Using Predictive Scoring Models
Woods, B., Adamson, D., Miel, S., & Mayfield, E. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098160 · Cited by 57 · see citing works
Self-citationVisual Stereotypes and Virtual Pedagogical Agents
Haake, M., & Gulz, A. (2008) · Lund University Publications (Lund University) · Cited by 53 · see citing works
RtGender: A Corpus for Studying Differential Responses to Gender
Voigt, R., Jurgens, D., Prabhakaran, V., Jurafsky, D., & Tsvetkov, Y. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3di79eiki43x · Cited by 52 · see citing works
Vygotsky and Education: Instructional Implications and Applications of Sociohistorical Psychology
Kozulin, A., & Moll, L. C. (1992) · The American Journal of Psychology · doi:10.2307/1423208 · Cited by 51 · see citing works
Cotos, E. (2014) · Iowa State University Digital Repository (Iowa State University) · Cited by 50 · see citing works
Generating Diagnostic Multiple Choice Comprehension Cloze Questions
Mostow, J., & Jang, H. (2012) · Cited by 48 · see citing works
Building Better Open-Source Tools to Support Fairness in Automated Scoring
Madnani, N., Loukina, A., von Davier, A. A., Burstein, J. C., & Cahill, A. (2017) · doi:10.18653/v1/w17-1605 · Cited by 45 · see citing works
Toward Non-Intuition-Based Machine and Artificial Intelligence Ethics
Hooker, J., & Kim, T. W. (2018) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3278721.3278753 · Cited by 45 · see citing works
Fairness in representation: quantifying stereotyping as a representational harm
Abbasi, M., Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2019) · Proceedings of the 2019 SIAM International Conference on Data Mining · doi:10.1137/1.9781611975673.90 · Cited by 44 · cited 1× · see citing works
“This may take the form of search results returning stereotypical images of minorities (Noble, 2018) or other algorithmic stereotyping (Abbasi et al., 2019); much of the work in word embeddings falls into this category (Caliskan et al., 2017), though research on downstream tasks and outcomes often…”
BackgroundCollege student perceptions of writing errors, text quality, and author characteristics
Johnson, A. C., Wilson, J., & Roscoe, R. D. (2017) · Assessing Writing · doi:10.1016/j.asw.2017.10.002 · Cited by 40 · see citing works
Rickford, A. E. (2001) · Journal of Education for Students Placed at Risk (JESPAR) · doi:10.1207/s15327671espr0604_4 · Cited by 37 · see citing works
Fagundes, D. D., Haynes, W. O., Haak, N. J., & Moran, M. J. (1998) · Language Speech and Hearing Services in Schools · doi:10.1044/0161-1461.2903.148 · Cited by 37 · see citing works
Automatic Generation of Challenging Distractors Using Context-Sensitive Inference Rules
Zesch, T., & Melamud, O. (2014) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.3115/v1/w14-1817 · Cited by 36 · see citing works
Building e‐rater® Scoring Models Using Machine Learning Methods
Chen, J., Fife, J. H., Bejar, I. I., & Rupp, A. A. (2016) · ETS Research Report Series · doi:10.1002/ets2.12094 · Cited by 35 · see citing works
CRISPR babies: a view from the centre of the storm
Lovell-Badge, R. H. (2019) · Development · doi:10.1242/dev.175778 · Cited by 34 · see citing works
What's in a Name? Reducing Bias in Bios without Access to Protected AttributesPreprint
Romanov, A., De‐Arteaga, M., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., Geyik, S. C., Kenthapadi, K., Rumshisky, A., & Kalai, A. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05233 · Cited by 34 · see citing works
Sommerauer, P., & Fokkens, A. S. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5430 · Cited by 33 · see citing works
Addressing Age-Related Bias in Sentiment Analysis
Díaz, M., Johnson, I. L., Lazar, A., Piper, A. M., & Gergle, D. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/852 · Cited by 32 · see citing works
Yu, H., Gui, L., Madaio, M., Ogan, A., Cassell, J., & Morency, L. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123413 · Cited by 31 · see citing works
Self-citationPaulo Freire and the Cold War Politics of Literacy
Kirkendall, A. J. (2010) · Project Muse (Johns Hopkins University) · Cited by 31 · see citing works
Why They Can't Write: Killing the Five-Paragraph Essay and Other Necessities
Warner, J. (2018) · Project Muse (Johns Hopkins University) · Cited by 30 · see citing works
Lubold, N., Walker, E., Pon-Barry, H., & Ogan, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93843-1_21 · Cited by 27 · see citing works
A Quantitative Study of Data in the NLP community
Mieskes, M. (2017) · doi:10.18653/v1/w17-1603 · Cited by 27 · see citing works
Dougherty, D., & Sharkey, J. D. (2017) · Children and Youth Services Review · doi:10.1016/j.childyouth.2017.01.021 · Cited by 26 · see citing works
Nojavanasghari, B., Hughes, C. E., & Morency, L. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053112 · Cited by 26 · see citing works
A Dataset and Classifier for Recognizing Social Media English
Blodgett, S. L., Wei, J. T.-Z., & O'Connor, B. (2017) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/w17-4408 · Cited by 26 · see citing works
An Analysis of Student Privacy Rights in the Use of Plagiarism Detection Systems
Brinkman, B. (2012) · Science and Engineering Ethics · doi:10.1007/s11948-012-9370-y · Cited by 25 · see citing works
Towards Enabling Feedback on Rhetorical Structure with Neural Sequence Models
Fiacco, J., Cotos, E., & Rosé, C. P. (2019) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3303772.3303808 · Cited by 24 · see citing works
EJ Extra: The Five-Paragraph Essay and the Deficit Model of Education
Brannon, L., Courtney, J. P., Urbanski, C., Woodward, S. V., Reynolds, J. M., Iannone, A. E., Haag, K., Mach, K., Manship, L., & Kendrick, M. (2008) · The English Journal · doi:10.58680/ej20086823 · Cited by 24 · see citing works
Burstein, J. C., Shore, J. R., Sabatini, J., Moulder, B., Lentini, J. E., Biggers, K., & Holtzman, S. L. (2014) · Journal of Educational Computing Research · doi:10.2190/ec.51.1.f · Cited by 22 · see citing works
African American Vernacular English and Education
Lanehart, S. L. (1998) · Journal of English Linguistics · doi:10.1177/007542429802600204 · Cited by 22 · see citing works
Analyzing Learned Representations of a Deep ASR Performance Prediction Model
Elloumi, Z., Besacier, L., Galibert, O., & Lecouteux, B. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5402 · Cited by 21 · see citing works
Beyond Compliance: Students and FERPA in the Age of Big Data
Parks, C. (2017) · Journal of Intellectual Freedom and Privacy · doi:10.5860/jifp.v2i2.6253 · Cited by 21 · see citing works
Revisiting Code-Switching Practice in TESOL: A Critical Perspective
Wang, H., & Mansouri, B. (2017) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-017-0359-9 · Cited by 18 · see citing works
Introspection for convolutional automatic speech recognition
Krug, A., & Stober, S. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5421 · Cited by 16 · see citing works
(2018) · National Conference on Artificial Intelligence · Cited by 16 · see citing works
Self-citationTowards a Framework for Smart Classrooms that Teach Instructors to Teach
Gerritsen, D., Zimmerman, J., & Ogan, A. (2018) · International Conference of Learning Sciences · Cited by 13 · see citing works
Self-citationPedagogy against the state: The ban on ethnic studies in Arizona
Wanberg, K. (2013) · Journal of Pedagogy / Pedagogický casopis · doi:10.2478/jped-2013-0002 · Cited by 12 · see citing works
Using exemplar responses for training and evaluating automated speech scoring systems
Loukina, A., Zechner, K., Bruno, J. V., & Klebanov, B. B. (2018) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w18-0501 · Cited by 12 · see citing works
Now We Stronger than Ever: African-American English Syntax in Twitter
Stewart, I. (2014) · Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop (EACL SRW) · doi:10.3115/v1/e14-3004 · Cited by 11 · see citing works
Role of Socio-cultural Differences in Labeling Students’ Affective States
Okur, E., Aslan, S., Alyüz, N., Esme, A. A., & Baker, R. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93843-1_27 · Cited by 10 · see citing works
Digital Blackface: The Repackaging of the Black Masculine Image
Green, J. L. (2006) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 7 · see citing works
Integrating the Management of Personal Data Protection and Open Science with Research Ethics
Lewis, D., Moorkens, J., & Fatema, K. (2017) · doi:10.18653/v1/w17-1607 · Cited by 6 · see citing works
On Qualitative and Quantitative Reasoning in Validity
Mislevy, R. J., Moss, P., & Gee, J. P. (2009) · Routledge eBooks · doi:10.4324/9780203885376-9 · Cited by 5 · see citing works
Bundled Gap Filling: A New Paradigm for Unambiguous Cloze Exercises
Wojatzki, M., Melamud, O., & Zesch, T. (2016) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w16-0519 · Cited by 4 · see citing works
Teacherbot: interventions in automated teaching
Bayne, S. (2018) · Apertura · doi:10.32870/ap.v10n2.1342 · Cited by 3 · see citing works
An English-Language Bibliography of the 1961 Cuban Literacy Campaign
Samuel, N. O., & Williams, K. E. (2016) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · doi:10.13140/rg.2.2.35817.70249 · Cited by 3 · see citing works
2. The Power of Chinese Linguistic Imperialism and Its Challenge to Multicultural Education
He, B. (2013) · Hong Kong University Press eBooks · doi:10.1515/9789888268290-007 · Cited by 2 · see citing works
Assessment for Teaching and Learning, Not Just Accountability
Gordon, E. W., & Rajagopalan, K. (2016) · Palgrave Macmillan US eBooks · doi:10.1057/9781137519962_2 · Cited by 1 · see citing works
Flagged first, then by citations
7 of the 192 references couldn’t be matched to a known paper.
Research works printed but not linked: 18
- Amy L Baylor and Yanghee Kim. 2005. Simulating instructional roles through pedagogical agents. In- ternational Journal of Artificial Intelligence in Edu- cation, 15(2):95-115.
- Kate Crawford. 2017. The trouble with bias, 2017. URL http://blog. revolutionanalytics. com/2017/12/the-trouble-with-bias-by-kate- crawford. html. Invited Talk by Kate Crawford at NIPS.
- Rotem Dror, Gili Baumer, Segev Shlomov, and Roi Re- ichart. 2018. The hitchhikers guide to testing statis- tical significance in natural language processing. In Proceedings of the 56th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 1383-1392.
- Paulo Freire. 1970. Pedagogy of the oppressed. Bloomsbury publishing USA.
- Kathleen Heugh. 1995. Disabling and enabling: Im- plications of language policy trends in south africa. Language and social history: Studies in South African sociolinguistics, pages 329-350.
- Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, and Hanna Wallach. 2018. Improving fairness in machine learning systems: What do industry practitioners need? arXiv preprint arXiv:1812.05239. · arXiv
- Andreas Krug and Sebastian Stober. 2018. Introspec- tion for convolutional automatic speech recogni- tion. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 187-199, Brussels, Bel- gium. Association for Computational Linguistics.
- Mike Levy and Glenn Stockwell. 2013. CALL dimen- sions: Options and issues in computer-assisted lan- guage learning. Routledge.
- Luis C Moll. 1992. Vygotsky and education: Instruc- tional implications and applications of sociohistori- cal psychology. Cambridge University Press.
- Jack Mostow and Hyeju Jang. 2012. Generating di- agnostic multiple choice comprehension cloze ques- tions. In Proceedings of the Seventh Workshop on Building Educational Applications Using NLP, pages 136-146. Association for Computational Lin- guistics.
- Burr Settles, Chris Brust, Erin Gustafson, Masato Hagiwara, and Nitin Madnani. 2018. Second lan- guage acquisition modeling. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 56-65.
- Mark D Shermis and Ben Hamner. 2012. Contrasting state-of-the-art automated scoring of essays: Analy- sis. In Annual national council on measurement in education meeting, pages 14-16.
- J Siegel. 2001. Pidgins, creoles, and minority dialect in education. Concise Encyclopedia of Sociolinguis- tics, Elsevier, Oxford, pages 747-749.
- Holly E Tatum, Beth M Schwartz, Peggy A Schim- moeller, and Nicole Perry. 2013. Classroom partic- ipation and student-faculty interactions: does gen- der matter? The Journal of Higher Education, 84(6):745-768.
- Candace West and Don H Zimmerman. 1987. Doing gender. Gender & society, 1(2):125-151.
- Eric Westervelt. 2015. Meet the mind-reading robo tutor in the sky. http://tinyurl.com/y3l4jk4a. NPR Morning Edition. Accessed 2019-04-13.
- Silke M Witt. 2012. Automatic error detection in pro- nunciation training: Where we are and where we need to go. Proc. IS ADEPT, 6.
- Diyi Yang, Tanmay Sinha, David Adamson, and Car- olyn Penstein Rosé. 2013. Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. In Proceedings of the 2013 NIPS Data-driven education workshop, volume 11, page 14. RE Yopp. 1988. Questioning and active comprehen- sion. Questioning Exchange, 2(3):231-238.
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“Furthermore, prior work has demonstrated that when human teachers’ scores are uncritically used as the “ground truth” for student performance, such assessment biases can easily be encoded into automated essay scoring systems used at scale (e.g., Madnani, Loukina, von Davier, Burstein, & Cahill, 2018; Mayfield et al.,…”
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Yang, Y., Gupta, A., Feng, J., Singhal, P., Yadav, V., Wu, Y., Natarajan, P., Hedau, V., & Joo, J. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534153 · Cited by 24 · see citing works
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Gârbacea, C., & Mei, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.15780 · Cited by 34 · see citing works
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Romero‐Hall, E. (2020) · Routledge eBooks · doi:10.4324/9780429260919 · Cited by 23 · see citing works
Lim, T., Gottipati, S., & Cheong, M. L. F. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00556-8 · Cited by 29 · see citing works
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Rosenberg, J. M., Lawson, M., Anderson, D. J., Jones, R. S., & Rutherford, T. A. (2020) · Routledge eBooks · doi:10.4324/9780429260919-7 · Cited by 18 · see citing works
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Barrett, L. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3871423 · Cited by 18 · see citing works
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Self-citationMcCarthy, K. S., Crossley, S. A., Meyers, K., Boser, U., Allen, L. K., Chaudhri, V. K., Collins‐Thompson, K., D’Mello, S. K., Choudhury, M. D., Garg, K., et al. (2022) · Technology Mind and Behavior · doi:10.1037/tmb0000063 · Cited by 17 · see citing works
Nawaz, S., Srivastava, N., Yu, J., Khan, A. A., Kennedy, G., Bailey, J., & Baker, R. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00242-6 · Cited by 16 · see citing works
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Shaffer, D. W., Wang, Y., & Ruis, A. R. (2025) · Journal of Learning Analytics · doi:10.18608/jla.2025.8423 · Cited by 13 · see citing works
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Lundgard, A. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372838 · Cited by 17 · see citing works
Lim, T., Gottipati, S., & Cheong, M. L. F. (2023) · Research Square · doi:10.21203/rs.3.rs-2696273/v1 · Cited by 14 · see citing works
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Stephenson, B., & Harvey, A. (2022) · Routledge eBooks · doi:10.4324/9781003293101-14 · Cited by 10 · see citing works
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Karumbaiah, S., & Brooks, J. (2021) · Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) · doi:10.1109/respect51740.2021.9620605 · Cited by 10 · see citing works
Riazy, S., Simbeck, K., & Schreck, V. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-86439-2_22 · Cited by 8 · see citing works
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Alwahaby, H., & Cukurova, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18851-0.00014-7 · Cited by 7 · see citing works
Gao, J., Yu, H., Cheung, Y., Cao, J., Wong, R. C.-W., & Zhang, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107754 · Cited by 5 · see citing works
Verständig, D., Kast, C., Stricker, J., Nürnberger, A., & Budrich, V. B. (2022) · Verlag Barbara Budrich eBooks · doi:10.3224/84742520 · Cited by 5 · see citing works
(2022) · Verlag Barbara Budrich eBooks · doi:10.2307/j.ctv2xh53x1 · Cited by 5 · see citing works
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Marras, M., Boratto, L., Ramos, G., & Fenu, G. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00271-1 · Cited by 62 · see citing works
Pinhanez, C. S., Fernandez, R., Grave, M., Nogima, J., & Hoory, R. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645165 · Cited by 3 · see citing works
Confronting Structural Inequities in AI for Education.Preprint
Madaio, M., Blodgett, S. L., Mayfield, E., & Dixon‐Román, E. J. (2021) · arXiv (Cornell University) · Cited by 3 · see citing works
Self-citationExploring Fairness in Automated Grading and Feedback Generation of Open-Response Math Problems
Gurung, A., & Heffernan, N. T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11647-6_12 · Cited by 2 · see citing works
Gârbacea, C., Guo, M., Carton, S., & Mei, Q. (2020) · arXiv (Cornell University) · Cited by 2 · see citing works
Villa, S. C., Craig, S. D., Zakhidov, D., & Zielke, M. A. (2021) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181321651253 · Cited by 1 · see citing works
Verständig, D., Kast, C., Stricker, J., & Nürnberger, A. (2022) · Verlag Barbara Budrich eBooks · doi:10.2307/j.ctv2xh53x1.3 · Cited by 1 · see citing works
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Patnaik, P. (2022) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-7998-9609-8.ch002 · Cited by 1 · see citing works
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Hipólito, I., & Podosky, P. (2024) · doi:10.31234/osf.io/gn7ur · Cited by 1 · see citing works
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text SimplificationPreprint
Gârbacea, C., Guo, M., Carton, S., & Mei, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.15823 · Cited by 1 · see citing works
Penteado, B. E. (2022) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/sbie.2022.225710
Auby, H., & Koretsky, M. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--44389
Practical Tools from Domain Adaptation for Designing Inclusive, Equitable, and Robust Generative AI
Sicilia, A., & Alikhani, M. (2023) · doi:10.18653/v1/2023.ijcnlp-tutorials.3
Kardam, N., Wilson, D. M., & Makhsous, S. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--46425
Transforming Student Assessments in the Era of Machine Learning
Ganesh, S. V., Suresh, V., Rajakarunakaran, S., Ganesan, L., & Suryakumar, V. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8292-9.ch017
Afterword: Towards sustainable technology use in instructed second language acquisition
Godwin‐Jones, R. (2025) · Language learning and language teaching · doi:10.1075/lllt.63.after
Campbell, L. D., & Robertson, N. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3276-5.ch012 · Cited by 1 · see citing works
Perspectives on AI-Enabled Education for Sustainable Development
Chand, A., Sarthi, S., Aanchal, & R., G. C. (2026) · Springer eBooks · doi:10.1007/978-3-032-28217-0_11
Sevhake, M., & Hofisi, C. (2026) · AI in Education · doi:10.3390/aieduc2030024 · Cited by 1 · see citing works
- Authors
- Mayfield, E., Madaio, M., Prabhumoye, S., Gerritsen, D., McLaughlin, B., Dixon-Román, E., & Black, A. W.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Mayfield, Elijah
- Venue
- Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Published
- 2019
- Pages
- 444-460
- OpenAlex
- W2971062439
- Cited by
- 62
- References
- 192
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 8 · 2022: 11 · 2021: 14 · 2020: 11
- FWCI
- 9.41
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Innovative Use of NLP for Building Educational Applications (publishedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- equity, bias, language technology, educational technology, algorithmic fairness
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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