Social networking-based learning (SN-learning) is one of the most promising innovations to promote learning via a social network, and thus, providing a more interactive, student-centered, cooperative, and on-demand environment. In such an environment, group formation plays an important role to the effectiveness of learning process. Adequate groups foster student interactions and increase learning outcomes. However, group formation is a complex task and requires automatic approaches to produce the optimal results in short time. To this direction, this paper presents a novel genetic algorithm for student grouping in an SN-learning system. Its innovations pertain to the attributes used for the composition of groups and genetic operators applied. In particular, student attributes refer to the three main dimensions of learning in an SN-learning environment: academic, cognitive, and social. Regarding genetic operators, the algorithm performs two crossover operators: a modification of two-point crossover and a new approach, called one-point per group crossover. Evaluating the proposed algorithm performance, the results show that it is more efficient than simple genetic algorithm approach, and considers a larger number of parameters than usual. Moreover, from the pedagogical perspective, a positive students’ attitude and high acceptance toward our group formation method is indicated.

中文翻译：

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基于社交网络学习中基于多特征的异质群体形成的增强遗传算法

基于社交网络的学习（SN学习）是通过社交网络促进学习的最有前途的创新之一，因此，它提供了一个更具交互性，以学生为中心，协作且按需的环境。在这样的环境中，小组的形成对学习过程的有效性起着重要的作用。足够的群体促进学生的互动并增加学习成果。但是，组队是一项复杂的任务，需要自动方法才能在短时间内产生最佳结果。为此，本文提出了一种新的遗传算法，用于SN学习系统中的学生分组。它的创新涉及用于组和所应用的遗传算子组成的属性。尤其是，学生属性是指在SN学习环境中学习的三个主要方面：学术，认知和社交。关于遗传算子，该算法执行两个交叉算子：对两点交叉的修改和一种称为每组一个点交叉的新方法。对所提出算法的性能进行评估，结果表明它比简单的遗传算法更有效，并且比常规方法考虑更多的参数。此外，从教学的角度，表明了学生对我们小组形成方法的积极态度和高度认可。对所提出算法的性能进行评估，结果表明它比简单的遗传算法更有效，并且比常规方法考虑更多的参数。此外，从教学的角度，表明了学生对我们小组形成方法的积极态度和高度认可。对所提出算法的性能进行评估，结果表明它比简单的遗传算法更有效，并且比常规方法考虑更多的参数。此外，从教学的角度，表明了学生对我们小组形成方法的积极态度和高度认可。