Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality

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Published Dec 25, 2016
DOI https://doi.org/10.5281/zenodo.3554593
Scott Crossley
Georgia State University
Laura K. Allen
Arizona State University
Erica L. Snow
Arizona State University
Danielle S. McNamara
Arizona State University

Abstract

This study investigates a novel approach to automatically assessing essay quality that combines natural language processing approaches that assess text features with approaches that assess individual differences in writers such as demographic information, standardized test scores, and survey results. The results demonstrate that combining text features and individual differences increases the accuracy of automatically assigned essay scores over using either individual differences or text features alone. The findings presented here have important implications for writing educators because they reveal that essay scoring methods can benefit from the incorporation of features taken not only from the essay itself (e.g., features related to lexical and syntactic complexity), but also from the writer (e.g., vocabulary knowledge and writing attitudes). The findings have implications for educational data mining researchers because they demonstrate new natural language processing approaches that afford the automatic assessment of performance outcomes.

How to Cite

Crossley, S., Allen, L. K., Snow, E. L., & McNamara, D. S. (2016). Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality. Journal of Educational Data Mining, 8(2), 1–19. https://doi.org/10.5281/zenodo.3554593
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Keywords

automated essay scoring, natural language processing, individual differences, intelligent tutoring systems, writing quality

References
ALLEN, L. K., SNOW, E. L., & MCNAMARA, D. S. (2014). The long and winding road: Investigating the differential writing patterns of high and low skilled writers. In J. Stamper, S. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (pp. 304-307). London, UK.

ALLEN, L. K., SNOW, E. L., & MCNAMARA, D. S. (2016). The narrative waltz: The role of flexibility on writing performance. Journal of Educational Psychology. doi: 10.1037/edu0000109

APPLEBEE, A. N., LANGER, J. A., JENKINS, L. B., MULLIS, I., & FOERTSCH, M. A. (1990). Learning to write in our nation’s schools. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement.

ATTALI, Y., & POWERS, D. (2008). A developmental writing scale. ETS Research Report Series, 2008(1). Princeton, NJ: ETS

BAKER, R., & SIEMENS, G. (2014). Educational data mining and learning analytics. In Sawyer, K. (Ed.) Cambridge Handbook of the Learning Sciences: 2nd Edition, pp. 253-274.

BAKER, R. S, DE CARVALHO, A. M., RASPAT, J., ALEVEN, V., CORBETT, A. T., & KOEDINGER, K. R. (2009). Educational software features that encourage and discourage "gaming the system". Proceedings of the 14th International Conference on Artificial Intelligence in Education, 475-482.

BAKER, R.S., & YACEF, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1 (1), 3-17.

BECK, J.E., CHANG, K., MOSTOW, J., CORBETT, A.T. (2008). Does help help? Introducing the Bayesian evaluation and assessment methodology. Proceedings of Intelligent Tutoring Systems, ITS 2008, 383–394.

BOWERS, A.J. (2010). Analyzing the longitudinal K-12 grading histories of entire cohorts of students: Grades, data driven decision making, dropping out and hierarchical cluster analysis. Practical Assessment, Research & Evaluation (PARE), 15(7), 1-18.

CROSSLEY, S. A., MCNAMARA, D. S., BAKER, R., WANG, Y., PAQUETTE, L., BARNES, T., & BERGNER, Y. (2015). Language to completion: Success in an educational data mining massive open online class. In Santos, O. C., Boticario, J. G., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J. M., Mihaescu, C., Moreno, P., Hershkovitz, A., Ventura, S., & Desmarais, M. (eds.) Proceedings of the 8th International Conference on Educational Data Mining (EDM). (pp. 388-392).

CROSSLEY, S. A., ROSCOE, R. D., MCNAMARA, D. S., & GRAESSER, A. C. (2011). Predicting human scores of essay quality using computational indices of linguistic and text features. In G. Biswas, S. Bull, J. Kay, & A Mitrovic (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education (pp. 438-440). Auckland, New Zealand: AIED.

EZEN-CAN, A., & BOYER, K. E. (2015). Understanding student language: An unsupervised dialogue act classification approach. Journal of Educational Data Mining, 7 (1), 51-78.

FERRARI, M., BOUFFARD, T., & RAINVILLE, L. (1998). What makes a good writer? Differences in good and poor writers’ self-regulation of writing. Instructional Science, 26, 473-488. doi:10.1023/A:1003202412203

FITZGERALD, J. & SHANAHAN, T. (2000). Reading and writing relations and their development. Educational Psychologist, 35, 39-50.

JING, S. (2015). Automatic grading of short answers for MOOC via semi-supervised document clustering. In Santos, O. C., Boticario, J. G., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J. M., Mihaescu, C., Moreno, P., Hershkovitz, A., Ventura, S., & Desmarais, M. (eds.) Proceedings of the 8th International Conference on Educational Data Mining (EDM).

MACGINITIE, W. H., & MACGINITIE, R. K. (1989). Gates-MacGinitie reading tests. Chicago: Riverside.

MCNAMARA, D. S., CROSSLEY, S. A., & MCCARTHY, P. M. (2010). The linguistic features of writing quality. Written Communication, 27, 57-86.

SAMEI, B., RUS, V., NYE, B., & MORRISON, D. (2015). Hierarchical dialogue act classification in online tutoring sessions, In Santos, O. C., Boticario, J. G., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J. M., Mihaescu, C., Moreno, P., Hershkovitz, A., Ventura, S., & Desmarais, M. (eds.) Proceedings of the 8th International Conference on Educational Data Mining (EDM).

VARNER, L. K., ROSCOE, R. D., & MCNAMARA, D. S. (2013). Evaluative misalignment of 10th-grade student and teacher criteria for essay quality: An automated textual analysis. Journal of Writing Research, 5, 35-59.

WEN, M., YANG, D., & ROSE, C. P. (2014a). Sentiment analysis in MOOC discussion forums: What does it tell us? In the Proceedings of the 7th International Conference on Educational Data Mining, 130-137.

WEN, M., YANG, D., & ROSE, C. P. (2014b). Linguistic reflections of student engagement in massive open online courses. In the Proceedings of the International Conference on Weblogs and Social Media.

WITTE, S., & FAIGLEY, L. (1981). Coherence, cohesion, and writing quality. College Composition and Communication, 32, 189-204.
Issue
Vol. 8 No. 2 (2016)
Section
Articles

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