Student Feedback

Student feedback analysis aims to efficiently extract actionable insights from often voluminous and unstructured data, primarily focusing on improving teaching practices and learning outcomes. Current research heavily utilizes large language models (LLMs) and natural language processing (NLP) techniques to summarize, categorize, and analyze student comments from various sources, including course evaluations and learning platform interactions. This work is significant because it facilitates more effective use of student feedback, enabling educators to refine their teaching methods and potentially improving student learning experiences at scale. The development of automated systems for processing and interpreting feedback offers a cost-effective and efficient solution to a long-standing challenge in education.