Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation

Abstract

For some decades now, theories on learning methodologies have advocated collaborative learning due to its good results in terms of effectiveness and learning types and its promotion of educational and social values. This means that teachers need to be able to apply different criteria when forming heterogeneous groups of students and to use automated techniques to assist them. In this study, we have created an approach based on complex network theory to design an algorithm called Minimum Entropy Collaborative Groupings (MECG) in order to form these heterogeneous groups more effectively. The algorithm was tested firstly under a synthetic framework and secondly in a real situation. In the first case, we generated 30 synthetic classrooms of different sizes and compared our approach with a genetic algorithm and a random grouping. In the latter case, the approach was tested on a group of 200 students on two subjects of a master’s degree in teacher training. For each subject there were 4 large groups of 50 students each, in which collaborative groups of 4 students were created. Two of these large groups were used as random groups, another group used the CHAEA test and the fourth group used the LML test. The results showed that the groups created with MECG were more effective, had less uncertainty and were more interrelated and mature. It was observed that the randomized groups did not obtain significantly better LML results and that this cannot be related to any emotional or motivational effect because the students performed the test as a placebo measure. In terms of learning styles, the results were significantly better with LML than with CHAEA, whereas no significant difference was observed in the randomized groups.

Enhancing Teamwork and Collaboration: A Systematic Review of Algorithm-Supported Pedagogical Methods

“…This section provides a focused review of the types of group formations achievable through algorithms, specifically utilizing the algorithms mentioned earlier (such as GAs) and the required characteristic attributes to form groups with homogeneous or heterogeneous features according to optimization functions. As shown in Figure 6, 15% of the studies adopted homogeneous grouping methods [14,19,35], while 30% of the research opted for heterogeneous group-ing approaches [11,12,26,32,42,43]. Most notably, over half of the studies explored mixed grouping methods that combined both homogeneous and heterogeneous characteristics, highlighting the significance and prevalence of mixed grouping methods in current research.…”

“…Genetic algorithm 14 [10][11][12][13][14][15]23,25,32,[35][36][37][38][39] Team formation algorithm based on coalition structure generation Belbin's theory and Bayesian learning 1 [26] Variable neighborhood search algorithm 1 [40] k-means algorithm 1 [19] Group algorithm 1 [41] Cluster and prune 1 [42] Minimum entropy collaborative grouping 1 [43] Furthermore, as demonstrated in Figure 4 and Table 3, among the selected articles for this analysis, six papers employed non-genetic algorithms. Each of these algorithms was unique, having been independently developed and applied, including the variable neighborhood search algorithm [40], the k-means algorithm [19], the group algorithm [41], the cluster and prune method [42] and minimum entropy collaborative grouping [43]. The diverse selection of algorithms in these studies not only enriches the methodological landscape of group formation but also offers a broader perspective and potential solutions for addressing specific grouping challenges.…”

“…As illustrated in Figure 6, six studies opted for heterogeneous grouping criteria [11,12,26,32,42,43], which accounted for 30% of the total. Based on Belbin's team role theory, Alberola et al [26] developed an artificial intelligence tool for creating heterogeneous teams in the classroom.…”

“…The vast majority of students reported receiving effective support from other group members during project work, indicating that the heterogeneous group configuration achieved the desired positive effects in practice. To create heterogeneous groups more efficiently, Toni and Ramon [43] designed an advanced algorithm called "minimum entropy collaborative grouping" based on complex network theory. The results indicate that groups formed through this algorithm are more efficient, exhibit lower uncertainty, have stronger interconnections and demonstrate a higher level of maturity.…”