Poster: Improving Formation of Student Teams: A Clustering Approach

Today's courses in engineering and other fields frequently involve projects done by teams of students. An important aspect of these team assignments is the formation of the teams. In some courses, teams select different topics to work on. Ideally, team formation would be included with topic selection, so teams could be formed from students interested in the same topics. Intuitive criteria for a team formation algorithm are that students should be assigned to (1) a topic which they have interest and (2) a team of students with similar interests in their topic. We propose an approach to meeting these criteria by mining student preferences for topics with a clustering approach and then matching them in groups to topics that suit their shared interests. Our implementation is based on hierarchical k-means clustering and a weighting formula that favors increasing overall student satisfaction and adding members until the maximum allowable team size is reached.