ABSTRACT

Students using a Learning Management System (LMS) as a learning support have been observed to demonstrate different learning behaviours. Studies have reported students exhibiting different procrastination tendencies, distinct social behaviours and system usage patterns. Students can be clustered together based on similarity in their learning behaviours. The K-means clustering algorithm is a simple and effective way to group students with similar behaviours. The authors use this algorithm in a novel way. It is applied in two phases on an unlabelled dataset obtained from LMS course logs. In the first phase, distinct clusters are formed using K-means. In the second phase, K-means is again applied to clusters obtained in the first phase to obtain further insight into students’ interaction behaviours. The two-phase application of K-means clustering clearly revealed the most and least successful learning behaviours. The authors also establish a relationship between observed behaviours and course final scores.

摘要

已经观察到使用学习管理系统 (LMS) 作为学习支持的学生表现出不同的学习行为。研究报告称，学生表现出不同的拖延倾向、不同的社交行为和系统使用模式。可以根据学生学习行为的相似性将学生聚集在一起。K-means 聚类算法是将具有相似行为的学生分组的一种简单而有效的方法。作者以一种新颖的方式使用该算法。它分两个阶段应用于从 LMS 课程日志中获得的未标记数据集。在第一阶段，使用 K-means 形成不同的集群。在第二阶段，K-means 再次应用于第一阶段获得的聚类，以进一步了解学生的互动行为。K-means 聚类的两阶段应用清楚地揭示了最成功和最不成功的学习行为。作者还建立了观察到的行为与课程最终分数之间的关系。