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首页> 外文期刊>International Journal of Emerging Technologies in Learning (iJET) >A Genetic-algorithm Approach for Balancing Learning Styles and Academic Attributes in Heterogeneous Grouping of Students
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A Genetic-algorithm Approach for Balancing Learning Styles and Academic Attributes in Heterogeneous Grouping of Students

机译：遗传算法在学生异类分组中平衡学习风格和学术属性的方法

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摘要

Cooperative learning is an instructional approach in which students work together in small groups in order to achieve a common academic goal. In the context of cooperative learning, students in classrooms tend to learn more by sharing their experiences and knowledge. In addition, a diversity of educational backgrounds and student learning styles can be used to build heterogeneous groups of students. In this paper, we propose an approach for the group composition, regarding the Index of Learning Styles (ILS) questionnaire and prior educational knowledge in order to achieve the mechanism for equity among groups and ensure that heterogeneous students are distributed optimally within the group formation. This causes the search for an optimized group composition of all students more complex and becomes a time-consuming task. Therefore, the proposed algorithm mimics the natural process of a genetic algorithm in order to achieve optimal solutions. In addition, we have implemented our algorithm to construct student groups. Our experiment shows that the algorithm enhances the quality of the group formation of heterogeneous students leading to better solutions.
机译：合作学习是一种教学方法，在这种方法中，学生们以小组形式共同努力，以实现共同的学术目标。在合作学习的背景下，课堂上的学生倾向于通过分享经验和知识来学习更多。此外，可以使用多种教育背景和学生学习风格来建立不同类型的学生群体。在本文中，我们针对学习风格指数（ILS）问卷和先验教育知识提出了一种小组组成方法，以实现小组之间的公平机制，并确保异类学生在小组形成中得到最佳分配。这导致寻找所有学生的最佳小组组成的工作变得更加复杂，并且成为一项耗时的工作。因此，提出的算法模仿了遗传算法的自然过程，以实现最优解。另外，我们已经实现了构建学生群体的算法。我们的实验表明，该算法提高了异类学生的小组形成质量，从而带来了更好的解决方案。

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