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ERIC Number: EJ1129844
Record Type: Journal
Publication Date: 2017-Mar
Pages: 20
Abstractor: As Provided
ISSN: ISSN-0735-6331
Filtering Essays by Means of a Software Tool: Identifying Poor Essays
Seifried, Eva; Lenhard, Wolfgang; Spinath, Birgit
Journal of Educational Computing Research, v55 n1 p26-45 Mar 2017
Writing essays and receiving feedback can be useful for fostering students' learning and motivation. When faced with large class sizes, it is desirable to identify students who might particularly benefit from feedback. In this article, we tested the potential of Latent Semantic Analysis (LSA) for identifying poor essays. A total of 14 teaching assistants evaluated a sample of N = 60 German essays. Using the human graders' evaluations as the standard of comparison, more of the poor essays were correctly identified by LSA than by random sampling (i.e., selecting essays by chance). By contrast, selection by text length did not perform better than random sampling. When three different teaching assistants evaluated another sample of N = 94 essays, the results largely replicated those found in the first sample. We conclude that LSA can help university teachers to identify poorly performing students. Additional analyses were computed to investigate the potential of combining the methods in different ways.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Germany
Grant or Contract Numbers: N/A