Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Abstract

Considering that group formation is one of the key processes when developing activities in collaborative learning contexts, this paper aims to propose a technique based on an approach of genetic algorithms to achieve homogeneous groups, considering the students' personality traits as grouping criteria. For its validation, an experiment was designed with 132 first semesters engineering students, quantifying their personality traits through the “Big Five Inventory”, forming workgroups and developing a collaborative activity in initial Programming courses. The experiment made it possible to compare the results obtained by the students applying the proposed approach to those obtained through other group formation strategies. It was demonstrated through the experiment that the homogeneous groups generated by the proposed technique produce better academic results compared to the grouping technique by students’ preference, traditionally used by the teachers when developing a collaborative activity.

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.…”

“…The following content elaborates on the innovative improvements made to GAs in various aspects in numerous studies. These enhancements include the optimization and adjustment of genetic operators, as seen in [10,14,25,32,35], aiming to enhance the search efficiency and quality of solutions of the algorithm. Additionally, some studies focused on refining the fitness functions [12,15], aiming to more accurately assess individual fitness and promote a more effective evolutionary direction.…”

“…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].…”

“…In this systematic review, only three studies exclusively utilized homogeneous grouping methods [14,19,35], accounting for 15% of the total. For instance, Oscar et al [35] introduced a strategy for forming homogeneous groups within collaborative learning settings aimed at enhancing cooperation and improving educational outcomes, both collectively and individually.…”

“…In this systematic review, only three studies exclusively utilized homogeneous grouping methods [14,19,35], accounting for 15% of the total. For instance, Oscar et al [35] introduced a strategy for forming homogeneous groups within collaborative learning settings aimed at enhancing cooperation and improving educational outcomes, both collectively and individually. The establishment of these groups relies on personality traits, specifically employing the Big-Five personality model (extraversion, agreeableness, conscientiousness, neuroticism and openness).…”