基于聚类算法的MOOCs学习者分类及学习行为模式研究

A Research on the Classification of Learners and Patterns of Learning Behavior Based on Cluster Algorithms under MOOCs' Environment

根据MOOCs平台上学习者的学习行为,利用基于离差平方和法的AHC层次聚类算法和K-means非层次聚类算法,对参加MOOCs课程的学习者进行了类别划分.研究和分析了不同类别学习者的学习行为对MOOCs学习效果的影响,并利用卡方检验和单因素方差分析对不同类型的学习者在课程完成率、课程成绩等方面做了详尽的对比分析.针对如何提高学生MOOCs学习效果及MOOCs课程的结构内容设计给出了建议,为进一步在高校中顺利开展MOOCs教育提供借鉴.

The learners who participate in the MOOC are classified by using of AHC and K-means, based on learners＇ learning behavior on MOOCs platform. Then, the impact of different learning behaviors on the MOOC＇s learning outcomes are studied and analyzed, and the course completion rate and course grade of different types of learners are compared and analyzed in detail by Chi-square test and one-way ANOVA. Finally, suggestions about how to improve the MOOCs learning effect and the design of the structure and content of MOOCs curricula are given. It also provides the reference for the further development of MOOCs education in colleges and universities.