Abstract
Literatures have indicated that well-balanced groups facilitate students’ learning performance in collaborative learning environments. For instructors, to construct well-balanced groups needs to take efforts and time to consider large number of students and characteristics. Hence, how to automatically construct well-balanced collaborative learning groups has been a popular issue for collaborative learning. This paper proposes a genetic algorithm (GA)-based grouping strategy to assist instructors in constructing inter-homogeneous and intra-heterogeneous collaborative learning groups considering multiple student characteristics. Several data sets with different problem sizes, such as number of students and characteristics, are employed as experimental materials. Experimental results have demonstrated that the proposed grouping method is effective, efficient and robust.
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Acknowledgment
The authors would like to thank Shun-Hsi Chung for his suggestions in this paper. The research is partially supported by the National Science Council of the Republic of China under the grant number NSC 102-2221-E-241-015.
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Tien, HW., Lin, YS., Chang, YC., Chu, CP. (2015). A Genetic Algorithm-Based Multiple Characteristics Grouping Strategy for Collaborative Learning. In: Chiu, D., et al. Advances in Web-Based Learning – ICWL 2013 Workshops. ICWL 2013. Lecture Notes in Computer Science(), vol 8390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46315-4_2
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DOI: https://doi.org/10.1007/978-3-662-46315-4_2
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