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ERIC Number: ED598690
Record Type: Non-Journal
Publication Date: 2019-Mar
Pages: 4
Abstractor: As Provided
Exploring Writing Analytics and Postsecondary Success Indicators
Burstein, Jill; McCaffrey, Daniel; Beigman Klebanov, Beata; Ling, Guangming; Holtzman, Steven
Grantee Submission, Paper presented at the International Conference on Learning Analytics & Knowledge (9th, Tempe, AZ, Mar 2019)
Writing is a challenge and a potential obstacle for students in U.S. 4-year postsecondary institutions lacking prerequisite writing skills. This study aims to address the research question: Is there a relationship between specific features (analytics) in coursework writing and broader success predictors? Knowledge about this relationship could contribute to more immediate personalized learning support for students. To investigate, we collected authentic coursework writing from students enrolled at one of six 4-year colleges. We then extracted natural language processing (NLP) writing features (analytics) from the writing samples and examined relationships between the analytics and college grade point average (GPA). Consistent with Burstein et al. (2017), findings suggest that NLP writing analytics may contribute to college GPA prediction. Our findings imply that real-time NLP writing analytics from authentic coursework writing could be used to efficiently track success and flag potential obstacles during students' college careers. [This paper was published in: "Companion Proceedings 9th International Conference on Learning Analytics & Knowledge" (LAK19), p.213-214, 2019.]
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: 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: R305A160115