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  • 标题:USING K-MEANS CLUSTERING TO MODEL STUDENTS’ LMS PARTICIPATION IN TRADITIONAL COURSES
  • 本地全文:下载
  • 作者:Klara Nelson
  • 期刊名称:Issues in Information Systems
  • 印刷版ISSN:1529-7314
  • 出版年度:2015
  • 卷号:16
  • 期号:4
  • 页码:102-110
  • 出版社:International Association for Computer Information Systems
  • 摘要:The focus of this research is on the relationship between student participation in a learning management system (LMS) in traditional course s and course grades using Blackboard Learn tracking data from two undergraduate courses taught by the author from January to May 2015. The results are consistent with prior re search that found a positive relationship between LMS partic ipation and student achievement. Correlation analysis showed significant and positive relationships between the students' course grade and their frequency of access overall as well as frequenc y of access to course materials. In addition, detailed LMS participation profiles were obtained from using k - means clustering, an unsupervised data mining method. The significant correlations between course grade and frequency of access variables are also evident in the 5 - cluster solution that emerged. Despite the small sample size, the present study shows the usefulness of k - means clustering , a data mining method, for better understanding students ' LMS participation
  • 关键词:Learning ; M ; anagement ; S ; ystems; ; ; LMS Participation; Academic Performance; ; Educational ; Data Mining ; ; ; ; ; k ; - ; Means Clustering
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