A hybrid approach for composing groups in collaborative learning contexts.

Considering that there are many alternatives in the literature for composing groups in collaborative learning contexts, we present a proposal that exhibits several features. First, and from the operational point of view, our proposal is highly flexible because i) it allows for several group sizes and an arbitrary array of grouping attributes, and ii) it may be easily adapted to consider several homogeneity/heterogeneity criteria. Second, and from the algorithmic point of view, it combines the best of two apparently opposite worlds: it uses a local brute-force search within an iterative process guided by a randomized heuristic criterion.