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

作　　者：马飞[1] 李娟[1] Ma Fei;Li Juan(School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)

机构地区：[1]北方民族大学计算机科学与工程学院,宁夏银川750021

出　　处：《广东工业大学学报》2018年第3期18-23,共6页Journal of Guangdong University of Technology

基　　金：宁夏回族自治区"十三五"重点专业(子项目):网络工程专业及子项目;北方民族大学2016年校级教育教学改革研究重点项目(2016JYZD01)

摘　　要：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.

关 键 词：MOOCs(Massive OPEN ONLINE Courses) 聚类算法 学习行为 学习效果 统计分析 

分 类 号：G434[文化科学—教育学]