Title: A comparative study of classifier ensembles for detecting inactive learner in university

Authors: Bayu Adhi Tama; Kyung-Hyune Rhee

Addresses: Department of IT Convergence and Application Engineering, Pukyong National University, Busan, South Korea ' Department of IT Convergence and Application Engineering, Pukyong National University, Busan, South Korea

Abstract: Prediction of undesirable learner's behaviours is an important issue in the distance learning system as well as the conventional university. This paper is devoted to benchmark ensemble of weak classifiers (decision tree, random forest, logistic regression, and CART) against single classifier models to predict inactive student. Two real-world datasets were obtained from a distance learning system and a computer science college in Indonesia. To evaluate the performance of the classifier ensembles, several performance metrics such as average accuracy, precision, recall, fall-out, F1, and area under ROC curve (AUC) value were involved. Our experiments reveal that classifier ensembles outperform single classifier in all evaluation metrics. This study contributes to the literature on making a comparative study of ensemble learners in the purview of educational data mining.

Keywords: classifier ensemble; educational data mining; EDM; distance learning; benchmark.

DOI: 10.1504/IJDATS.2018.095215

International Journal of Data Analysis Techniques and Strategies, 2018 Vol.10 No.4, pp.351 - 368

Received: 29 Mar 2016
Accepted: 10 Jan 2017

Published online: 02 Oct 2018 *

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