Learning to Prevent Inactive Student of Indonesia Open University

Bayu Adhi Tama

Learning to Prevent Inactive Student of Indonesia Open University

Bayu Adhi Tama, Journal of Information Processing Systems Vol. 11, No. 2, pp. 165-172, Jun. 2015

https://doi.org/10.3745/JIPS.04.0015
Keywords: Educational Data Mining, Ensemble Techniques, Inactive Student, Open University
Fulltext:

Abstract

The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.

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Cite this article

[APA Style]

Tama, B. (2015). Learning to Prevent Inactive Student of Indonesia Open University. Journal of Information Processing Systems, 11(2), 165-172. DOI: 10.3745/JIPS.04.0015.

[IEEE Style]

B. A. Tama, "Learning to Prevent Inactive Student of Indonesia Open University," Journal of Information Processing Systems, vol. 11, no. 2, pp. 165-172, 2015. DOI: 10.3745/JIPS.04.0015.

[ACM Style]

Bayu Adhi Tama. 2015. Learning to Prevent Inactive Student of Indonesia Open University. Journal of Information Processing Systems, 11, 2, (2015), 165-172. DOI: 10.3745/JIPS.04.0015.