A Genetic Algorithm-Based Multiple Characteristics Grouping Strategy for Collaborative Learning
A Genetic Algorithm-Based Multiple Characteristics Grouping Strategy for Collaborative Learning
复制标题
一种基于遗传算法的协作学习多特征分组策略
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Chih
中科院分区：
文献类型：
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作者：
Hui;Yu;Yi;Chih
文献指出，在合作学习环境中，均衡的小组有利于学生的学习表现。对于辅导员来说，要构建均衡的小组，需要花时间和精力去考虑学生的数量和特点。因此，如何自动建构均衡的合作学习群组，已成为合作学习的热门议题。本文提出了一种基于遗传算法（GA）的分组策略，以帮助教师构建间同质和内异质协作学习小组，考虑到多个学生的特点。本研究以学生人数、学生特征等不同问题规模的数据集作为实验材料。实验结果表明，该方法是有效的，高效和鲁棒性。
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.