Incorporating learning characteristics into automatic essay scoring models: What individual differences and linguistic features tell us about writing quality.

Scott Crossley, Laura K Allen, Erica L Snow, Danielle S McNamara


This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new 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 both educators and researchers 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). Such findings expand our knowledge of textual and non-textual features that are predictive of writing success.

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