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ERIC Number: EJ1125807
Record Type: Journal
Publication Date: 2016
Pages: 19
Abstractor: As Provided
ISSN: EISSN-2157-2100
Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality
Crossley, Scott A.; Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S.
Journal of Educational Data Mining, v8 n2 p1-19 2016
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.
International Working Group on Educational Data Mining. e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: High Schools; Secondary Education
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
Identifiers - Location: Arizona
Identifiers - Assessments and Surveys: Gates MacGinitie Reading Tests
IES Funded: Yes
Grant or Contract Numbers: R305A080589; R305G020018