ERIC Number: ED585777
Record Type: Non-Journal
Publication Date: 2015-Mar
Pages: 5
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-
EISSN: N/A
Pssst… Textual Features… There Is More to Automatic Essay Scoring than Just You!
Crossley, Scott; Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S.
Grantee Submission, Paper presented at the International Learning Analytics & Knowledge (LAK) Conference (Poughkeepsie, NY, Mar 16-20, 2015)
This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new approaches that measure student attributes such as demographic information, standardized test scores, and survey results. The results demonstrate that combining both text features and student attributes leads to essay scoring models that are on par with state-of-the-art scoring models. Such findings expand our knowledge of textual and nontextual features that are predictive of writing success. [This paper was published in: "LAK '15" (Poughkeepsie, New York, March 16-20, 2015). ACM. (ISBN 978-1-4503-3417-4)]
Publication Type: Speeches/Meeting Papers; 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 (Phoenix)
Identifiers - Assessments and Surveys: Gates MacGinitie Reading Tests; Writing Apprehension Test
IES Funded: Yes
Grant or Contract Numbers: R305A080589; R305G020018