首页    期刊浏览 2024年11月29日 星期五
登录注册

文章基本信息

  • 标题:Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining
  • 作者:Yajuan Li
  • 期刊名称:International Journal of Emerging Technologies in Learning (iJET)
  • 印刷版ISSN:1863-0383
  • 出版年度:2018
  • 卷号:13
  • 期号:10
  • 页码:108-120
  • 语种:English
  • 出版社:Kassel University Press
  • 摘要:This paper aims to predict the user dropout rate in MOOC learning based on the features extracted from user learning behaviours. For this purpose, some learning behaviour features were extracted from the data of MOOC platforms. Then two machine learning algorithms, respectively based on support vector machine (SVM) and the artificial neural network (ANN), were introduced to predict the dropout rate of MOOC course. The two algorithms were contrasted with some commonly used prediction methods. The comparison results show that our algo-rithms outperformed others in the prediction of MOOC user dropout rate. The re-search sheds new light on the feature extraction and learning effect of MOOC programs.
  • 关键词:massive open online course (MOOC);Feature extraction;Machine learning;Learning behaviour analysis
Loading...
联系我们|关于我们|网站声明
国家哲学社会科学文献中心版权所有