First, do no harm: automated detection of abusive comments in student evaluation of teaching surveys
Author:
Affiliation:
1. Queensland University of Technology, Brisbane, Queensland, Australia
Publisher
Informa UK Limited
Subject
Education
Link
https://www.tandfonline.com/doi/pdf/10.1080/02602938.2022.2081668
Reference36 articles.
1. Student feedback systems in higher education: a focused literature review and environmental scan
2. Student evaluation of instruction: What can be learned from students’ written comments?
3. Regularising LSTM classifier by transfer learning for detecting misogynistic tweets with small training set
4. The Student as a Commentator: Students’ Comments in Student Evaluations of Teaching
5. Exploring Bias in Student Evaluations: Gender, Race, and Ethnicity
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