Capturing fine-grained teacher performance from student evaluation of teaching via ChatGPT

Abstract

Student evaluation of teaching (SET) is a vital component of educational enhancement, yet conventional assessment tools face inherent limitations. While open-ended questions provide a platform for students to convey authentic sentiments, the absence of automated labeling tools poses a challenge in the case of large-scale applications. In response, this study undertakes a comprehensive exploration, centered on the utilization of ChatGPT for capturing fine-grained teacher performance from SET. Based on a collected dataset and manual coding, the performance of ChatGPT with various strategies including zero-shot and few-shot, and some supervised models, including CNN, LSTM and BERT, are evaluated and compared. As a result, ChatGPT exhibits the promise of achieving commendable performance with a small number of labeled samples. This approach reduces the dependency on extensive labeled data, offering an effective solution. However, in terms of performance, a discernible margin persists in comparison to advanced supervised models, BERT. Our study also acknowledges there are various factors, such as task complexity and prompt clarity, influencing ChatGPT’s performance and consistency. In summation, while the integration of ChatGPT into practical SET applications holds significant promise, further explorations are imperative to ensure the alignment of its capabilities with the intricate demands.

Cited as:

Zhang, B., & Tian, X. (2024). Capturing fine-grained teacher performance from student evaluation of teaching via ChatGPT. Education andLifelong Development Research, 1(4), 166-179. https://doi.org/10.46690/elder.2024.04.01

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