Applying Genetic Algorithms for Recommending Adequate Competitors in Mobile Game-Based Learning Environments

Mobile game-based learning (MGBL) exploits an entertaining environment for providing digital education. Such an approach involves the construction of students' groups for gaming towards advancing their knowledge. However, building adequate groups has important pedagogical implications, since the recommendation of appropriate collaborators could further enhance the students' cognitive abilities. Towards this direction, this paper presents a MGBL application for the tutoring of computer programming. In this application, the system recommends to each student four peers to play with as competitors using genetic algorithm. The genetic algorithm finds the most adequate peers for each student by taking into consideration students' learning modality, previous knowledge, current knowledge and misconceptions. As such, the student can select from the list one person from the proposed ones, who share common characteristics. The two main reasons why homogeneous groups are chosen to be formed are to promote fair competition and to provide adaptive game content based on players' characteristics for improving their learning outcomes. Our MGBL application was evaluated using students' t-test with promising results.