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- W2528832023 abstract "For effective cooperative learning, facilitators and teachers need to organize classroom activities into an enabling environment that enhance academic and social learning experiences. The first task is to carry out a social network analysis to provide a measure of the social status of the learners. This is then followed by creation of learner groups, aimed at optimizing, as much as possible, the cooperative learning experiences of the learners, subject to heterogeneous grades, social status, and gender restrictions. This is a complex process that demands the application of global optimization approaches that take advantage of the grouping structure of the problem. A grouping genetic algorithm (GGA) is proposed for the problem. The approach utilizes grouping operators to model and address the problem in its various scenarios. The application of the GGA is extended to include cooperative learning problems that allow varying group sizes. Comparative computational results demonstrate that the proposed GGA is more effective and efficient than previous approaches." @default.
- W2528832023 created "2016-10-14" @default.
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- W2528832023 date "2016-10-05" @default.
- W2528832023 modified "2023-09-27" @default.
- W2528832023 title "Grouping Learners for Cooperative Learning: Grouping Genetic Algorithm Approach" @default.
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- W2528832023 doi "https://doi.org/10.1007/978-3-319-44394-2_6" @default.
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