Leveraging large language models for scalable analysis of the end-of-the-course student feedback

Abstract

Analyzing open-ended student feedback in course evaluations is a laborintensive task due to the unstructured and complex nature of natural language. While Large Language Models (LLMs) offer significant potential for automation, a welldefined methodology for their application in analyzing student feedback remains underdeveloped. This paper addresses this gap by proposing an LLM-based feedback analytics pipeline designed to transform students’ open-ended feedback into structured, actionable insights. The pipeline consists of three sequential stages: (i) segmenting student feedback into semantic units and assigning polarity (sentiment) to those units; (ii) topical classification of semantic units, and (iii) summarization of units within each topical category. By systematizing these processes, the proposed method enables educators and course managers to efficiently derive meaningful patterns from vast datasets of student opinions. We evaluated the proposed method using a comprehensive dataset from several editions of a U.S. university course, yielding encouraging results of the method’s effectiveness. This research provides a scalable, generic methodology for (semi-)automated feedback analysis, ultimately supporting data-informed improvements in teaching and course management.