Zixuan Wang, Paul Denny, Juho Leinonen, Andrew Luxton-Reilly
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引用次数: 0
摘要
本研究调查了大型语言模型（特别是 ChatGPT）的使用情况，以分析用于收集学生对教学质量反馈的总结性评价工具（SET）的反馈。我们发现，这些模型提高了对 SET 分数的理解能力，并增强了语境对学生评价的影响。这项工作旨在揭示学生评价数据中隐藏的模式，为自动详细分析学生反馈迈出了积极的第一步。
Leveraging Large Language Models for Analysis of Student Course Feedback
This study investigates the use of large language models, specifically ChatGPT, to analyse the feedback from a Summative Evaluation Tool (SET) used to collect student feedback on the quality of teaching. We find that these models enhance comprehension of SET scores and the impact of context on student evaluations. This work aims to reveal hidden patterns in student evaluation data, demonstrating a positive first step towards automated, detailed analysis of student feedback.
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