Discussion
The first aim of this work was to create and develop an automatic heterogeneous group creation tool based on complex networks. We called the resulting tool MECG (Minimum Entropy Collaborative Groupings). In doing so, we seem to be addressing a real need regarding the most effective way to divide up big groups heterogeneously. One of the reasons for this, as Hwang et al. [Konert et al. [Forming groups in a non-randomized way must consider the relationship between different students in a classroom. Complex networks are appropriate to solve this kind of problem, with which we can define the relationship between the different students. Then the choice of this relationship determines which kind of groups we would like to obtain. In our approach, we focus on learning styles/patterns (using CHAEA/LML questionnaires) plus gender, but our approach is flexible enough to permit the use of any questionnaire. In this way, the constraints (or attributes) are not closed to a specific choice as is the case in previous attempts at automatic group formation that have used, for instance, fixed ontologies [There is a tendency to use distance computational methods such as genetic algorithms to assess the group formation problem [Regarding the experiment, we provide a robust framework when including the same students in both the control and the experimental groups (in different classrooms), thus we can compare the performance of the groups with the same student in a paired t-test and reduce external factors. In contrast, most formation group approaches that were validated involving real students, have built the control and experimental groups with different students [Our approach to automatic group formation is not the first one related to complex networks, but as far as we are concerned, our study is the first to use entropy measure for this purpose. Moreover, our approach differs from others that only use complex networks science to obtain a measure of the quality of the groups [We have shown that MECG provides students groupings with lower entropy with respect to those provided by a random grouping or a genetic algorithm, and it was shown in several situations (different classroom sizes, 3-member or 4-member groups, synthetic students generated given CHAEA or LML questionnaire usual answers). It is reasonable that our approach provides lower entropies since it was built for that purpose. We wondered what would happen if we computed the fitness value defined in Moreno et al. [As a first step, in this work we present a first comparison in a real situation between our approach and a random grouping. Results in 