A multi-objective evolutionary algorithm-based soft computing model for educational data mining A distance learning experience

PurposeThe 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...

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Veröffentlicht in:AAOU journal Jg. 12; H. 1; S. 106 - 123
Hauptverfasser: Tan, Choo Jun, Lim, Ting Yee, Bong, Chin Wei, Liew, Teik Kooi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bingley Emerald Group Publishing Limited 02.05.2017
Schlagworte:
Academic Achievement
Adult Basic Education
Adult Students
Case studies
Cluster analysis
Data mining
Distance learning
Educational Attainment
Educational materials
Educational research
Educational Resources
Genetic algorithms
Internet
Open educational resources
School Holding Power
Statistical methods
Student Participation
Student retention
Teachers
ISSN:1858-3431, 2414-6994, 2414-6994
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Zusammenfassung:PurposeThe 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/approachA 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.FindingsThe 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/valueA soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1858-3431
2414-6994
2414-6994
DOI:10.1108/AAOUJ-01-2017-0012