Abstract

The current study explored the relationship between learner profiles and the nature of their interaction with content in massive, open, and online learning environments. The research was conducted on the Anadolu University Open Education System, and data from 597,164 learners enrolled in 86 different degree programs were analyzed by unsupervised machine learning methods. Cluster analysis was used to identify learner profile groups and association rules were applied to identify learner-content interaction patterns. As a result of the analyses, five clusters were obtained, and it was determined that the attribute with the highest discrimination in determining the clusters was the learners’ semester grade point average. The clusters were named according to learner-content interactions and the learners’ semester grade point average. Analysis of the association rules revealed that various learner-content interactions emerged in the context of profile groups.

摘要

当前的研究探讨了学习者档案与他们在大规模、开放和在线学习环境中与内容交互的性质之间的关系。该研究是在阿纳多卢大学开放教育系统上进行的，通过无监督机器学习方法分析了 86 个不同学位课程的 597,164 名学习者的数据。使用聚类分析来识别学习者概况组，并应用关联规则来识别学习者与内容的交互模式。分析的结果是，获得了五个聚类，并且确定在确定聚类时具有最高区分度的属性是学习者的学期平均成绩点。这些集群是根据学习者与内容的互动和学习者的学期平均成绩来命名的。