比较母语与非母语者在在线编程课中互评的语气差异
Using Sentiment Analysis to Investigate Peer Feedback by Native and Non-Native English Speakers
- 用Twitter-roBERTa模型分析500名学生的互评情感倾向
- 非母语者写评语更积极，但收到的反馈更不友好
- 语言背景影响互评体验，性别和年龄也有交互作用
美国高校计算机硕士项目中，2023年60.2%的学位授予国际学生。许多学生参与线上课程，通过同伴互评提升学习参与度与教学效果。由于课程以英语授课，不少学生在非母语环境中学习。本文研究母语与非母语英语者在在线计算机课程中的同伴反馈体验差异，采用Twitter-roBERTa模型分析随机抽样的500名学生所撰写的同伴反馈情感。结果表明，母语者对反馈评价更低，而非母语者虽写作更积极，却收到的反馈情感评分更低。控制性别与年龄后，仍存在显著交互效应，表明语言背景在塑造同伴反馈体验中起着微妙而复杂的作用。
原文摘要 · Abstract (English)
Graduate-level CS programs in the U.S. increasingly enroll international students, with 60.2 percent of master's degrees in 2023 awarded to non-U.S. students. Many of these students take online courses, where peer feedback is used to engage students and improve pedagogy in a scalable manner. Since these courses are conducted in English, many students study in a language other than their first. This paper examines how native versus non-native English speaker status affects three metrics of peer feedback experience in online U.S.-based computing courses. Using the Twitter-roBERTa-based model, we analyze the sentiment of peer reviews written by and to a random sample of 500 students. We then relate sentiment scores and peer feedback ratings to students' language background. Results show that native English speakers rate feedback less favorably, while non-native speakers write more positively but receive less positive sentiment in return. When controlling for sex and age, significant interactions emerge, suggesting that language background plays a modest but complex role in shaping peer feedback experiences.
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