A multi-objective evolutionary algorithm-based soft computing model for educational data mining: A distance learning experience. Issue 1 (2nd May 2017)

• Record Type: Journal Article
• Authors: Tan, Choo Jun; Lim, Ting Yee; Bong, Chin Wei; Liew, Teik Kooi
• Is Part Of: AAOU journal. Volume 12:Issue 1(2017)
• Page Start: 106
• Page End: 123
• Publication Date: 2017-05-02
• Subjects: Classification -- Optimization -- Student achievement -- Educational data mining -- Multi-objective evolutionary algorithm; Open learning -- Asia -- Periodicals; Distance education -- Asia -- Periodicals; 371.35095
• DOI: 10.1108/AAOUJ-01-2017-0012
• Languages: English
• ISSNs: 2414-6994

Abstract

Abstract : Purpose: The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students' online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach: A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide. Findings: The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value: A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.