基于互补度的队友分组与遗传算法的社会网络分析

Huang-Ming Su, T. Shih, Yung-Hui Chen
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引用次数: 4

摘要

在过去的一年中,合作学习已成为最重要的教学策略之一。帮助学习者进行适当的分组变得越来越重要。为了解决这个问题,人们提出了许多方法。在本文中,我们采用一种新颖的方法,考虑学习者的学习状态和社会网络的互补程度,以增强学习者之间的互动和团队合作。此外,本文还采用遗传算法(GA)来产生更好的分组结果。通过记录学习者的学习状态,可以动态调整每次作业的分组结果。结果表明,该方法能较好地优化分组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Grouping Teammates Based on Complementary Degree and Social Network Analysis Using Genetic Algorithm
In the past year, Cooperative Learning has become one of the most important teaching strategies. Helping learners group appropriately is now becoming more and more important. To solve the problem, a lot of methods have been proposed. In this paper, we employ a novel approach that considers the complementary degree of learner's learning state and social networks to enhance interaction and teamwork between learners. Moreover, this paper using genetic algorithm (GA) to generate better grouping results. By recording the learning statuses of learners, we can adjust grouping result from each assignment dynamically. Results show that the proposed approach can optimize the grouping well.
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