Effect of the Multiple Intelligences in multiclass predictive model of computer programming course achievement

Conference proceedings article

Authors/Editors

Strategic Research Themes

No matching items found.

Publication Details

Author list: Ninrutsirikun U., Watanapa B., Arpnikanondt C., Phothikit N.

Publication year: 2017

Start page: 297

End page: 300

Number of pages: 4

ISBN: 9781509025961

ISSN: 2159-3442

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015394980&doi=10.1109%2fTENCON.2016.7848010&partnerID=40&md5=fced28a9891bb4230fee79bdb2b9b37c

Languages: English-Great Britain (EN-GB)

View on publisher site

Abstract

This paper proposes the measurement of Multiple Intelligences (or MI) value as a co-determiner combine with the traditional academic achievements in predicting student performance in taking a computer programming course. The effectiveness of MI in such a predictive model is tested on three machine learning algorithms: Artificial Neural Network, Support Vector Machine, and the classic Na๏ve Bayes. Using three different validation schemes: 2, 5, and 10-folded cross validations, the results show that the Mi-inclusive model significantly helps to improve the accuracy of predicting students' performance. These are divided into three class: good, average, and poor achievement. ฉ 2016 IEEE.

Keywords

Computer Programming, Multiple Intelligences, Predicive Model, Student's Performance