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

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Issue
Vol. 8 No. 2 (2016)
Section
Articles

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