Abstract

End of semester student evaluations of teaching are the dominant mechanism for providing feedback to academics on their teaching practice. For large classes, however, the volume of feedback makes these tools impractical for this purpose. This paper explores the use of open-source generative AI to synthesise factual, actionable and appropriate summaries of student feedback from these survey responses. In our setup, we have 742 student responses ranging over 75 courses in a Computer Science department. For each course, we synthesise a summary of the course evaluations and actionable items for the instructor. Our results reveal a promising avenue for enhancing teaching practices in the classroom setting. Our contribution lies in demonstrating the feasibility of using generative AI to produce insightful feedback for teachers, thus providing a cost-effective means to support educators' development. Overall, our work highlights the possibility of using generative AI to produce factual, actionable, and appropriate feedback for teachers in the classroom setting.
OriginalsprogEngelsk
TitelProceedings of the 52nd Annual Conference of the European Society for Engineering Education (SEFI)
ForlagEuropean Society for Engineering Education
Publikationsdatosep. 2024
Sider1-10
DOI
StatusUdgivet - sep. 2024
BegivenhedSEFI 2024 - EPFL, Lausanne, Schweiz
Varighed: 2 sep. 20245 sep. 2024
https://sefi2024.eu/

Konference

KonferenceSEFI 2024
LokationEPFL
Land/OmrådeSchweiz
ByLausanne
Periode02/09/202405/09/2024
Internetadresse

Fingeraftryk

Dyk ned i forskningsemnerne om 'Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers'. Sammen danner de et unikt fingeraftryk.

Citationsformater