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Cooperative learning is a pedagogical approach in which students collaborate in small groups to attain a shared academic objective. In the classroom, cooperative learning aims to enhance learning outcomes by promoting the exchange of information, social, and personal resources among students. Group formation is a critical and complex step that significantly impacts the effectiveness of cooperative learning. In this article, we propose a novel approach for constructing cooperative learning groups that employs machine learning to predict student performance and incorporates the most common grouping strategies to recommend optimal group formation.