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ERIC Number: ED586432
Record Type: Non-Journal
Publication Date: 2015
Pages: 5
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-
EISSN: N/A
Available Date: N/A
Predicting Misalignment between Teachers' and Students' Essay Scores Using Natural Language Processing Tools
Allen, Laura K.; Crossley, Scott A.; McNamara, Danielle S.
Grantee Submission, Paper presented at the International Conference on Artificial Intelligence in Education (17th, 2015)
We investigated linguistic factors that relate to misalignment between students' and teachers' ratings of essay quality. Students (n = 126) wrote essays and rated the quality of their work. Teachers then provided their own ratings of the essays. Results revealed that students who were less accurate in their self-assessments produced essays that were more causal, contained less meaningful words, and had less argument overlap between sentences.
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: High Schools; Secondary Education; Grade 10; Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305A080589; R305G020018
Author Affiliations: N/A