Abstract
Group formation is a significant process of collaborative learning. Collaborative learning helps individuals to be more focused on the purpose, goals, and approach related to the task that they have been assigned. Groups are incontrovertibly beneficial for many of the intricate objectives of learning, related to critical thinking, decision making, problem-solving, preserving, or modifying attitudes. The success of the group undoubtedly depends on the compatibility and aptness of members of the group. This paper attempts to solve this group formation problem using optimized genetic algorithm. The proposed method combines both static and dynamic characteristics of students to achieve productive collaborative learning groups. The system is implemented and the experimental results are shown for a varied number of class size. The algorithm gives the solution to the problem in time and achieves better quality leading to a better solution for forming groups of students having heterogeneous characteristics. The performance has also competed with the k-means clustering algorithm for the same set of characteristic features.
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Sarode, N., Bakal, J.W. (2021). Toward Effectual Group Formation Method for Collaborative Learning Environment. In: Karuppusamy, P., Perikos, I., Shi, F., Nguyen, T.N. (eds) Sustainable Communication Networks and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-15-8677-4_29
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DOI: https://doi.org/10.1007/978-981-15-8677-4_29
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