Leveraging Large Language Models for Analysis of Student Course Feedback

被引：1
|
作者：
Wang, Zixuan [1 ]
Denny, Paul [1 ]
Leinonen, Juho [1 ]
Luxton-Reilly, Andrew [1 ]
机构：
[1] Univ Auckland, Auckland, New Zealand
来源：
PROCEEDINGS OF THE 16TH ANNUAL ACM INDIA COMPUTE CONFERENCE, COMPUTE 2023 | 2023年
关键词：
Student Evaluation of Teaching; Large Language Model; Student Feedback; Natural Language Processing;
D O I：
10.1145/3627217.3627221
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：76 / 79
页数：4
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