Using feature selection and association rule mining to evaluate digital courseware

Shaveen Singh, Sunil Pranit Lal

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

3 Citations (Scopus)


Effective digital courseware should be easy to implement and integrate into instructional plans, saving teachers time and helping them support their students' learning needs. It should also not only enable students to achieve explicit learning objectives but also accelerate the pace at which they do so. This paper highlights the advantage of using Feature Selection techniques and Associative rule mining to get insightful knowledge from the log data from the Learning Management System (Moodle). The Machine Learning approach can be objectively deployed to obtain a predictive relationship and behavioral aspects that permits mapping the interaction behaviour of students with their course outcome. The knowledge discovered could immensely assist in evaluating and validating the various learning tools and activities within the course, thus, laying the groundwork for a more effective learning process. It is hoped that such knowledge would result in more effective courseware that provides for a rich, compelling, and interactive experience that will encourage repeated, prolonged, and self-motivated use.

Original languageEnglish
Title of host publicationProceedings - 2013 11th International Conference on ICT and Knowledge Engineering, ICT and KE 2013
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)9781479922574
Publication statusPublished - 2013
Externally publishedYes
EventIEEE International Conference on ICT and Knowledge Engineering 2013 - Bangkok, Thailand
Duration: 20 Nov 201322 Nov 2013
Conference number: 11th

Publication series

NameInternational Conference on ICT and Knowledge Engineering
ISSN (Print)2157-0981
ISSN (Electronic)2157-099X


ConferenceIEEE International Conference on ICT and Knowledge Engineering 2013
Abbreviated titleICT and KE 2013


  • association rule mining
  • attribute ranking
  • e-learning
  • machine learning
  • online development

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