Formation of learning groups in cMoocs using particle swarm optimization

In this work we developed an algorithm to form collaborative groups on Massive Online Open Courses (Moocs) using Particle Swarm Optimization (PSO) method. Group learning principles are used in this work as an attempt to overcome the dichotomy that exists between the collective, which involves the formation of an online learning community on a massive scale, and the individual, with different interests, prior knowledge and expectations. The proposed PSO algorithm accomplishes the task of forming groups based on two criteria, level of knowledge and interest, thus forming groups with different levels and similar interests, providing better students´ interactions and knowledge construction. Results of computational tests showed that the algorithm can meet the criteria for grouping in a satisfactory computation time and it is more efficient than algortithms for group formation commonly approached in the literature. Computational tests have also shown that the algorithm is robust taking into account various data sets and variations of iterations.