Quantifying the mismatch between course content and students' dialogue in online learning environments

Abstract

Due to the internet’s increasing global availability, online learning has become a new paradigm for distance learning in higher education. While student interactions and reactions are readily observable in a physical classroom environment, monitoring student interactions and quantifying divergence between lecture topics and the topics that interest students are challenging in online learning platforms. Understanding the effects of this divergence is important for monitoring student engagement and aiding instructors, who are focused on improving the quality of their online courses. The authors of this paper propose a topic modeling method, based on latent Dirichlet allocation (LDA), that quantifies the effects of divergence between course topics (mined from textual transcriptions) and student-discussed topics (mined from discussion forums). Correlations between the measured dissimilarities and (a) the number of posts and comments in discussion forums, (b) the number of submitted assignments, and (c) students’ average performance scores are presented. A case study involving video lecture transcripts and discussion forum posts/comments in a massive open online course (MOOC) platform demonstrates the proposed method’s potential success and informs course providers about the challenges of measuring the topics that interest students.