Effect of the Multiple Intelligences in Multiclass Predictive Model of Computer Programming Course Achievement

被引：0

作者：

Ninrutsirikun, Unhawa ^{[1
]}

Watanapa, Bunthit ^{[1
]}

Arpnikanondt, Chonlameth ^{[1
]}

Phothikit, Naphongthawat ^{[1
]}

机构：

[1] King Mongkuts Univ Technol Thonburi, Sch Informat Technol, Bangkok, Thailand

来源：

PROCEEDINGS OF THE 2016 IEEE REGION 10 CONFERENCE (TENCON) | 2016年

关键词：

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

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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 Naive 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.

引用

页码：297 / 300

页数：4

共 22 条

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