This paper explores the use of Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) to assist instructors in identifying course-wide student challenges through topic modeling. Unlike previous studies that primarily generate personalized resources for individual students, this research focuses on analyzing reflections from an entire class to inform curriculum design and intervention strategies. Using the LLaMa-3.1-8B model, experiments across varying cosine similarity thresholds reveal both the strengths and limitations of integrating retrieval-based models. While RAG did not consistently outperform standalone LLMs, it offers key insights into the complexities of applying retrieval- augmented approaches in educational settings.