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Multi-split optimized bagging ensemble model selection for multi-class educational data mining

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Abstract

Predicting students’ academic performance has been a research area of interest in recent years, with many institutions focusing on improving the students’ performance and the education quality. The analysis and prediction of students’ performance can be achieved using various data mining techniques. Moreover, such techniques allow instructors to determine possible factors that may affect the students’ final marks. To that end, this work analyzes two different undergraduate datasets at two different universities. Furthermore, this work aims to predict the students’ performance at two stages of course delivery (20% and 50% respectively). This analysis allows for properly choosing the appropriate machine learning algorithms to use as well as optimize the algorithms’ parameters. Furthermore, this work adopts a systematic multi-split approach based on Gini index and p-value. This is done by optimizing a suitable bagging ensemble learner that is built from any combination of six potential base machine learning algorithms. It is shown through experimental results that the posited bagging ensemble models achieve high accuracy for the target group for both datasets.

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Acknowledgments

This study was funded by Ontario Graduate Scholarship (OGS) Program.

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Authors and Affiliations

  1. Electrical & Computer Engineering Department, University of Western Ontario, London, ON, Canada

    MohammadNoor Injadat, Abdallah Moubayed, Ali Bou Nassif & Abdallah Shami

  2. Computer Engineering Department, University of Sharjah, Sharjah, UAE

    Ali Bou Nassif

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  1. MohammadNoor Injadat
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Injadat, M., Moubayed, A., Nassif, A.B. et al. Multi-split optimized bagging ensemble model selection for multi-class educational data mining. Appl Intell 50, 4506–4528 (2020). https://doi.org/10.1007/s10489-020-01776-3

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