This paper is published in Volume-6, Issue-5, 2020
Area
Computer Science
Author
Olukoya Bamidele Musiliu
Org/Univ
Federal University of Oye-Ekiti, Nigeria, Nigeria
Pub. Date
28 October, 2020
Paper ID
V6I5-1348
Publisher
Keywords
EDM, Ensemble, Boosting, Random Forest, Data Mining, Classifiers, Machine Learning

Citationsacebook

IEEE
Olukoya Bamidele Musiliu. Using ensemble random forest, boosting and base classifiers to ameliorate prediction of students’ academic performance, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Olukoya Bamidele Musiliu (2020). Using ensemble random forest, boosting and base classifiers to ameliorate prediction of students’ academic performance. International Journal of Advance Research, Ideas and Innovations in Technology, 6(5) www.IJARIIT.com.

MLA
Olukoya Bamidele Musiliu. "Using ensemble random forest, boosting and base classifiers to ameliorate prediction of students’ academic performance." International Journal of Advance Research, Ideas and Innovations in Technology 6.5 (2020). www.IJARIIT.com.

Abstract

In recent time, educational data mining (EDM) has received substantial considerations. Many techniques of data mining have been proposed to dig out out-of-sight knowledge in educational data. The Knowledge obtained assists the academic institutions to further enhance their process of learning and methods of passing knowledge to students. Education Data Mining have been playing substantial role in predicting student’s academic performance. In this study, a novel student’s performance prediction model premised on techniques of data mining with Students’ Essential Features (SEF). Students’ Essential Features (SEF) are linked to the learner’s interactivity with the e-learning management system. The performance of student’s predictive model is assessed by set of classifiers, viz. Bayes Network, Logistic Regression and REP Tree. Consequently, ensemble methods of Boosting and Random Forest using WEKA as an Open Source Tool are applied to improve the performance of these single classifiers. The results obtained reveal that there is a robust affinity between learner’s behaviours and their academic attainment. Results from the study shows that REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.
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