Clustering Models for Topic Analysis in Graduate Discussion Forums

Abstract

Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This paper proposes the use of clustering models and interactive visualizations to conduct a qualitative analysis of graduate discussion forums. Our goal is to identify the sub-topics and topic evolutions in the discussion forums by applying text mining techniques. Our approach generates insights into the topic analysis in the forums and discovers the students’ cognitive understanding within and beyond the classroom learning settings. We developed the analysis model and conducted our experiments on a graduate course in Information Systems. The results show that the proposed techniques are useful in discovering knowledge from the forums and generating user-friendly visualizations. Such results can be used by the faculty to analyse the students’ discussions and study the strengths and weaknesses of the students’ cognitive knowledge on course topics.