ERIC Number: ED624053
Record Type: Non-Journal
Publication Date: 2022
Pages: 7
Abstractor: As Provided
ISBN: N/A
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EISSN: N/A
Available Date: N/A
Going beyond "Good Job": Analyzing Helpful Feedback from the Student's Perspective
Rashid, M. Parvez; Xiao, Yunkai; Gehringer, Edward F.
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022)
Peer assessment can be a more effective pedagogical method when reviewers provide quality feedback. But what makes feedback helpful to reviewees? Other studies have identified quality feedback as focusing on detecting problems, providing suggestions, or pointing out where changes need to be made. However, it is important to seek students' perspectives on what makes a review helpful to a reviewee. This study explores the helpfulness of feedback from students' perspectives when the feedback contained suggestions or mentioned problems or both. We applied natural language processing techniques to identify suggestions and problems mentioned in peer reviews. We also analyzed important text features that are associated with suggestions or problems detected by the peer feedback. The result showed that students are likely to find a review helpful if a suggestion is provided along with the problem mentioned in the feedback rather than simply identifying the problem. [For the full proceedings, see ED623995.]
Descriptors: Peer Evaluation, Feedback (Response), Natural Language Processing, Artificial Intelligence, College Students, Student Attitudes, Models
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: North Carolina
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