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

Ma Fei; Li Juan

doi:10.12052/gdutxb.180027

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

更新时间：2026-05-27

- A Research on the Classification of Learners and Patterns of Learning Behavior Based on Cluster Algorithms under MOOCs’ Environment
- Vol. 35, Issue 3, Pages: 18-23(2018)
- 作者机构：
  
  北方民族大学　计算机科学与工程学院,宁夏,银川,750021
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.180027
  CLC： G434
- Online First：09 May 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
马飞, 李娟. 基于聚类算法的MOOCs学习者分类及学习行为模式研究[J]. 广东工业大学学报, 2018, 35(3): 18-23.

Ma Fei, Li Juan. A Research on the Classification of Learners and Patterns of Learning Behavior Based on Cluster Algorithms under MOOCs’ Environment[J]. 2018, 35(3): 18-23.
马飞, 李娟. 基于聚类算法的MOOCs学习者分类及学习行为模式研究[J]. 广东工业大学学报, 2018, 35(3): 18-23. DOI： 10.12052/gdutxb.180027.

Ma Fei, Li Juan. A Research on the Classification of Learners and Patterns of Learning Behavior Based on Cluster Algorithms under MOOCs’ Environment[J]. 2018, 35(3): 18-23. DOI： 10.12052/gdutxb.180027.

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.

Abstract

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.

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Keywords

references

MOU Z J, WU F T. The Exploration of learning outcome prediction indicators and analysis of learning group characteristics for MOOC [J]. Modern Distance Education Research, 2017(3): 58-66.

MARGARYAN A, MANUELA B. Instructional quality of massive open online courses (MOOCs) [J]. Computers Education, 2015(80): 77-83.

WU Y H. General overview on clustering algorithms [J]. Computer Science, 2015(6): 491-499.

ZHU M C, WANG W Z, HUANG J S. Improved initial cluster center selection in K-means clustering [J]. Engineering Computations, 2014, 31(8): 1661-1667.

JIANG Z X, ZHANG Y, LI X M. Learning behavior analysis and prediction based on MOOC data [J]. Journal of Computer Research and Development, 2015, 52(3): 614-628.

LI S, ZHANG Y F, YU G, et al . Learning behavior acquisition and analysis of MOOC [J ] . China Science Paper, 2015(20): 2373-2376.

JIANG S Y, WANG L X. Some challenges in clustering analysis[J].Journal of Guangdong University of Technology, 2014, 31(3):32-38.

LU OWEN H T, HUANG JEFF C H, HUANG ANNA Y Q, et al . Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course [J ] . Interactive Learning Environments, 2017, 25(2): 220-234.

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