Forming Heterogeneous Groups for Intelligent Collaborative Learning Systems with Ant Colony Optimization

Heterogeneity in learning groups is said to improve academic performance. But only few collaborative online systems consider the formation of heterogeneous groups. In this paper we propose a mathematical approach to form heterogeneous groups based on personality traits and the performance of students. We also present a tool that implements this mathematical approach, using an Ant Colony Optimization algorithm in order to maximize the heterogeneity of formed groups. Experiments show that the algorithm delivers stable solutions which are close to the optimum for different datasets of 100 students. An experiment with 512 students was also performed demonstrating the scalability of the algorithm.