The aim of this paper is to present a method that uses computational intelligence techniques to classify students according to the principles of differentiated instruction. A clustering algorithm based on particle swarm optimization is applied to two sets of data emerging from the holistic assessment of the student's particular characteristics and needs. The results illustrate the algorithm's contribution to the effective formation of heterogeneous student groups, with the members of each having homogeneous characteristics of skills, difficulties, psychosocial and cognitive profiles. Thus, the teacher can easily manage students, by knowing the characteristics of each group. A comparison with a genetic algorithm as well as cuckoo search algorithm shows that the proposed method provides improved categorization capabilities.

中文翻译：

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使用粒子群优化为差异化教学的应用形成学习者的自动组

本文的目的是提出一种方法，该方法使用计算智能技术根据差异化教学的原则对学生进行分类。基于粒子群优化的聚类算法应用于从学生的特定特征和需求的整体评估中产生的两组数据。结果说明了该算法对有效形成异质学生群体的贡献，每个群体的成员在技能、困难、社会心理和认知特征方面具有同质特征。因此，教师可以通过了解每个组的特点轻松管理学生。与遗传算法以及布谷鸟搜索算法的比较表明，所提出的方法提供了改进的分类能力。