First, do no harm: automated detection of abusive comments in student evaluation of teaching surveys

IF 4.1 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Samuel Cunningham, Melinda Laundon, A. Cathcart, M. A. Bashar, R. Nayak
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引用次数: 3

Abstract

ABSTRACT Student evaluation of teaching (SET) surveys are the most widely used tool for collecting higher education student feedback to inform academic quality improvement, promotion and recruitment processes. Malicious and abusive student comments in SET surveys have the potential to harm the wellbeing and career prospects of academics. Despite much literature highlighting abusive feedback in SET surveys, little research attention has been given to methods for screening student comments to identify and remove those that may cause harm to academics. This project applied innovative machine learning techniques, along with a dictionary of keywords to screen more than 100,000 student comments made via a university SET during 2021. The study concluded that these methods, when used in conjunction with a final stage of human checking, are an effective and practicable means of screening student comments. Higher education institutions have an obligation to balance the rights of students to provide feedback on their learning experience with a duty to protect academics from harm by pre-screening student comments before releasing SET results to academics.
第一,不伤害:在学生评价教学调查中自动检测辱骂性评论
摘要:学生教学评估(SET)调查是收集高等教育学生反馈的最广泛使用的工具,用于为学术质量的提高、晋升和招聘过程提供信息。SET调查中学生的恶意和辱骂言论有可能损害学术界的健康和职业前景。尽管许多文献强调SET调查中的滥用反馈,但很少有研究关注筛选学生评论的方法,以识别和删除那些可能对学术造成伤害的评论。该项目应用了创新的机器学习技术,以及一本关键词词典,筛选了2021年通过大学SET发表的超过10万条学生评论。该研究得出结论,当这些方法与人类检查的最后阶段结合使用时,是筛选学生评论的有效和可行的方法。高等教育机构有义务在学生对其学习经历提供反馈的权利与保护学者免受伤害的义务之间取得平衡,在向学者发布SET结果之前,对学生的评论进行预先筛选。
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来源期刊
Assessment & Evaluation in Higher Education
Assessment & Evaluation in Higher Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
11.20
自引率
15.90%
发文量
70
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