Investigation of Group Formation using Low Complexity Algorithms

Designing tools that support group formation is a challenging goal for both the areas of adaptive and collaborative e-learning environments. Group formation may be used for a variety of purposes such as for grouping students that could potentially benefit from cooperation based on their individual characteristics or needs, for mediating peer help by matching peer learners, for facilitating instructors proposing an initial grouping approach. In this paper, we discuss several factors that need to be considered when assigning learners to groups. We also investigate the use of the c-means family clustering algorithms and uniform distribution, for group formation. The fuzzy c-means is compared to (a) the k-means algorithm for homogenously grouping students, and (b) a random selection algorithm (based on the uniform distribution) for formulating heterogeneous groups. Preliminary results from grouping 36 students based on 2 and 3 criteria, indicate the potential of the fuzzy c-means algorithm for homogenously grouping students, and the random selection algorithm as a low complexity approach for achieving a significant level of heterogeneity.