基于学习风格模型的学生分组优化方法  

Student Grouping Optimization Based on Learning Styles

作　　者：张永韡[1] 肖琴[2] 齐亮[1] 薛文涛[1] ZHANG Yongwei;XIAO Qin;QI Liang;XUE Wentao(College of Electronics and Information,Jiangsu University of Science and Technology,Zhenjiang 212003,China;Information Construction and Management Office,Jiangsu University of Seience and Technology,Zhenjiang 212003,China)

机构地区：[1]江苏科技大学电子信息学院,镇江212003 [2]江苏科技大学信息化建设与管理办公室,镇江212003

出　　处：《电气电子教学学报》2022年第2期32-36,共5页Journal of Electrical and Electronic Education

基　　金：江苏省现代教育技术研究课题(2021-R-93695);江苏科技大学教学方法改革示范课建设项目;江苏省终身教育研究课题(21SZJB006)。

摘　　要：分组学习通过组内成员的交流与优势互补实现更高效的学习。但随机分组或指定分组很难全面考虑学习风格对学习效果的影响。本文应用Felder-Silverman学习风格模型表达学习者特征,以组内学习风格多样性和组间学习风格均衡性为目标,采用人工蜂群算法优化分组。对比固定分组,优化分组可有效兼顾组内多样性和组间公平性。通过对比小组任务和个人任务得分率,证实了优化的分组可提高学习效果。Group learning achieves more efficient learning through the communications of members in the group and the complementary advantages.However,it isn′t easy to fully consider the influence of learning style on the learning effect in the form of random grouping or designated grouping.In this paper,the Felder-Silverman learning style model is used to represent learner characteristics.The objective is to maximize learning style diversity within groups and to balance average learning styles among groups.The artificial bee colony(ABC)algorithm is used to optimize grouping.Compared with fixed grouping,optimized grouping can effectively take into account the diversity within groups and fairness between groups.The comparison between the scoring rate of group tasks and individual tasks confirms that the optimized grouping can improve the learning effect.

关 键 词：学习风格 分组学习 人工蜂群算法 

分 类 号：G40-057[文化科学—教育学原理]