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Using feature selection and association rule mining to evaluate digital courseware

Singh, Shaveen and Lal, Sunil P. (2013) Using feature selection and association rule mining to evaluate digital courseware. [Conference Proceedings]

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Abstract

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.

Item Type: Conference Proceedings
Additional Information: DOI: 10.1109/ICTKE.2013.6756286
Uncontrolled Keywords: Internet;courseware;educational courses;human computer interaction;learning (artificial intelligence),MOOC;Web-based learning management systems;courseware development;e-planning tool;educational courseware evaluation;interaction log data;machine learning technique;massive open online courses;student evaluation survey;student online interaction behaviour;Conferences;Courseware;Discussion forums;Educational institutions;Electronic learning;Least squares approximations;Prediction algorithms;Artificial Intelligence;attribute ranking;e-learning;machine learning;online development
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: Shaveen Singh
Date Deposited: 16 Apr 2014 09:49
Last Modified: 07 Jul 2016 11:51
URI: http://repository.usp.ac.fj/id/eprint/7277
UNSPECIFIED

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