Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions
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As online discussion boards become a popular medium for collaborative problem solving, we would like to understand patterns of group interactions that lead to collaborative learning and better performance In this paper, we present an approach for assessing collaboration in online discussion, by profiling student-group participation We use a modularity function to compute optimal discussion group partitions and then examine usage patterns with respect to high-versus low-participating students, and high- versus low-performing students as measured by grades We apply the profiling technique to a discussion board of an undergraduate computer science course with three semesters of discussion data, comprising 142 users and 1620 messages Several patterns are identified, and in particular, we show that high achievers tend to act as ‘bridges', engaging in more diverse discussions with a wider group of peers.