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Mining patterns of events in students’ teamwork data

It is difficult, but very important, to learn to work effectively as part of a team. One potentially invaluable source of information about the success, or problems, in the way that teams learn can be drawn from the electronic traces of their collaborations. The paper describes data mining of student group interaction data to identify significant sequences of activity. Our goal is to build tools that can flag interaction sequences indicative of problems, so that we can use these to assist student teams in early recognition of problems. We also want tools that can identify patterns that are markers of success so that these might indicate improvements during the learning process. Our first challenge is to transform the raw data available in large quantities, preprocessing it into a suitable alphabet for use in data mining. Then, we need data mining algorithms that can properly account for the temporal nature of the data and the character of group interaction. We envisage that this may involve a two way process, where theories of effective group behaviour can drive the data mining and, in the opposite direction, that the data mining should provide results that are meaningful to groups wishing to improve their effectiveness. We report the results of our work in the context of a semester long software development project course.