Connecting and Profiling Participants
within an Online Discussion Board
USC/Information Science Institute
2009. 12. 28
302-308, 11:00 AM
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. We apply the profiling technique to a discussion board of an undergraduate computer science course. 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. In order to promote collaborative interactions, the Mentor Match tool identifies student mentors, i.e., peers with a relatively good understanding of a particular topic. The system identifies students who often provide answers on a given topic and encourages classmates to invite mentors to participate in related discussions.
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