Online course refinement through association rule mining

A. Y.K. Chan, K. O. Chow, K. S. Cheung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Online courses are widely used to support teaching and learning in higher education. To improve their quality, instructors must continuously refine them according to the students' needs. Questionnaires may be used to gather student feedback but it is subjective, expensive and timeconsuming. Web server log files, which are recorded automatically, provide a cheaper and quicker way to gather user access data. However, existing web log analysis software does not provide sufficient or relevant information about student usage in online courses. This paper proposes to refine online courses from student usage pattern through association rule mining. Association rule mining is applied on web server log files of 14 online courses. Results show that the discovery of student usage patterns can be used for the refinement of online courses.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Web-Based Education, WBE 2005
Pages322-326
Number of pages5
Publication statusPublished - 2005
Externally publishedYes
Event4th IASTED International Conference on Web-Based Education, WBE 2005 - Grindelwald, Switzerland
Duration: 21 Feb 200523 Feb 2005

Publication series

NameProceedings of the IASTED International Conference on Web-Based Education, WBE 2005

Conference

Conference4th IASTED International Conference on Web-Based Education, WBE 2005
Country/TerritorySwitzerland
CityGrindelwald
Period21/02/0523/02/05

Keywords

  • Association rule mining
  • Online courses
  • Web usage mining

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