A Semi-Pareto Optimal Set based algorithm for grouping of students

B. Mahdi, Taghiyareh Fattaneh
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引用次数: 6

Abstract

Collaborative learning in traditional and e-learning environments has a significant influence on the student learning process. Forming appropriate groups of students is one of the important factors in the collaborative learning, so lots of researches have been done in this area. In this paper, the new algorithm is proposed for grouping of students that uses modified Pareto Optimal Set concept that is called Semi-Pareto Optimal Set. The main advantages of this algorithm is that it does not limit number of student attributes in forming the groups and can uses for both heterogeneous and homogeneous groups. The results indicate that the proposed algorithm has high quality in formation groups and formed groups that are efficient in two below criteria 1) intra-group fitness that is the difference between students of groups and 2) inter- group fitness that is similarity between formed groups.
基于半pareto最优集的学生分组算法
传统和网络学习环境下的协作学习对学生的学习过程有显著的影响。形成合适的学生群体是协作学习的重要因素之一,因此在这方面做了大量的研究。本文提出了一种新的学生分组算法,该算法采用改进的帕累托最优集概念,即半帕累托最优集。该算法的主要优点是不限制分组中学生属性的数量,可以用于异构和同质分组。结果表明,该算法在编队群和编队群中具有较高的质量,在两个方面都是有效的:1)群内适应度,即群内学生之间的差异;2)群间适应度,即编队群之间的相似性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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