Analysis of student activity in web-supported courses as a tool for predicting dropout

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Persistence in learning processes is perceived as a central value; therefore, dropouts from studies are a prime concern for educators. This study focuses on the quantitative analysis of data accumulated on 362 students in three academic course website log files in the disciplines of mathematics and statistics, in order to examine whether student activity on course websites may assist in providing early identification of learner dropout from specific courses or from degree track studies in general. It was found in this study that identifying the changes in student activity during the course period could help in detecting at-risk learners in real time, before they actually drop out from the course. Data examination on a monthly basis throughout the semester can enable educators and institutions to flag students that have been identified as having unusual behavior, deviating from the course average. It was found that a large percentage of students (66%) who had been marked as at-risk actually did not finish their courses and/or degree. The presented analysis allows instructors to observe website student usage data during a course, and to locate students who are not using the system as expected. Furthermore, it could enable university decision makers to see the information on a campus level for initiating intervention programs.

Original languageEnglish
Pages (from-to)1285-1304
Number of pages20
JournalEducational Technology Research and Development
Issue number5
StatePublished - 1 Oct 2017


  • Learning Management Systems
  • Learning analytics
  • Predicting course dropout
  • Web-supported Learning

All Science Journal Classification (ASJC) codes

  • Education


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