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Educational courseware evaluation using machine learning techniques

Singh, Shaveen and Lal, Sunil P. (2013) Educational courseware evaluation using machine learning techniques. [Conference Proceedings]

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Abstract

With the introduction of massive open online courses (MOOCs) and other web-based learning management systems (LMS), there is a greater need to develop methods for exploring the unique types of data that come from the educational context. This paper highlights the advantage of using Machine Learning (ML) as an e-planning tool to enhance learning and improve courseware development. Researchers generally consider student evaluation survey on courses to be highly reliable and at least moderately valid on courseware evaluation. However, low response rate, retaliation, grades and comparison with past instructors sometimes affects the reliability of the result. ML algorithms has been deployed in this paper to intelligently examine the interaction log data from the LMS to obtain a predictive map that permits mapping the online interaction behaviour of students with their course outcome. These predictive relationships are then investigated and ranked using various ML algorithms to evaluate and validate the various learning tools and activities, and their effectiveness within the course.

Item Type: Conference Proceedings
Additional Information: DOI: 10.1109/IC3e.2013.6735969
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;
Subjects: L Education > LC Special aspects of education
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: Shaveen Singh
Date Deposited: 16 Apr 2014 09:43
Last Modified: 07 Jul 2016 11:50
URI: http://repository.usp.ac.fj/id/eprint/7278
UNSPECIFIED

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