Automatic Analysis of Online Course Discussion Forum: A Short Review

Course discussion forums play a vital role in connecting students to their peers, exchanging ideas, opinions, and information in online learning. These forums are not only the key point of contact for the course, but also facilitate student's learning. In this paper, we present a short review of applications of machine learning and natural language processing techniques to analyze course discussion posts to provide insights and improve students' learning outcome. We categorized these methods into four main groups based on the area of applications: automated question answering systems, thread recommender systems, conversational agents, and topic modeling. The methods in automated question answering systems focus on identifying common questions, concerns, and confusion among learners and generating responses without human intervention. The methods in thread recommender systems focus on identifying and recommending useful threads to the students. The methods of conversational agents focus on creating virtual agents to provide personalized support to students in a natural conversation. The topic modeling group focuses on identifying the topics mostly discussed by the students. The research findings indicate that the course forum analysis techniques can be integrated in a logical way into smart learning environments which can transform the effectiveness and accessibility of online courses. Such integrations could improve online learning experience of the students by providing more personalized, meaningful, and engaging educational and instructional supports.