Abstract
Student evaluation of teaching (SET) is an ad-hoc way of assessing teaching effectiveness in higher education institutions. In this paper, we present an approach to analyzing sentiments expressed in SET comments using a large language model (LLM). By employing natural language processing techniques, we extract and analyze sentiments expressed by students when the course has ended, aiming to provide educators and administrators with valuable insights into teaching quality and elements to improve teaching practice. Our study demonstrates the effectiveness of LLMs in sentiment analysis of comments, highlighting their potential to enhance the evaluation process. Our experiments with a crowdsourced tagged dataset show a 93% of accuracy in the classification of feedback messages. We discuss the implications of our findings for educational institutions and propose future directions for research in this domain.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
