Enhancing Student's Performance Classification Using Ensemble Modeling





Classification, Ensemble Model, Machine Learning, Stacking Classifier, Student Performance


 A precise prediction of student performance is an important aspect within educational institutions to
improve results and provide personalized support of students. However, the predication accuracy of student
performance considers an open issue within education field. Therefore, this paper proposes a developed approach
to identify performance of students using a group modeling. This approach combines the strengths of multiple
algorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM).
Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robust
and reliable predictive model because it considers The experiments were evaluated using the Open University
Learning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive dataset
containing various characteristics related to the student’s activities thereby five cases based on the utilized dataset
were investigated. The experiment results showed that the proposed ensemble model presented its ability with
accurate results to classify student performance by achieving 95% of accuracy rate. As a result, the proposed model
exceeded the accuracy of individual basic models by using the strengths of various algorithms to improve the
generalization by reducing the potential weaknesses of individual models. Consequently, the education institutes
can easily identify students who may need additional support and interventions to improve their academic


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Author Biography

Ahmed Adil Nafea, Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, Iraq.

I was born in Al-Anbar, Iraq on October 7, 1995. I received my Master's degree from Universiti Kebangsaan Malaysia,, Malaysia in 2020 under the supervision of Prof. Nazlia Omar. My, master thesis is titled “Adverse Drug Reaction Detection Using Latent Semantic Analysis”. I hold a Bachelor's degree from the University of Anbar, Iraq. My research interest is in the areas of Data Science, Deep Learning, Machine Learning, Natural Language Processing, Artificial Intelligence, Computer Vision, Reinforcement Learning, Data Analysis, Statistical Techniques, Data Preprocessing, Big Data, Data Visualization, and Linear Algebra. I'm one of the top students at the university kebangssan Malaysia. I was awarded the medal of honour and distinction as one of the best students in the world in the top 400 high-ranking international universities of the year 2020 according to the statistics of the Golden Key International Honour Society. I'm one of the top students at the University of Anbar I was ranked (4) out of (51) graduates. I am a reviewer in the journal of computer science - Scopus indexed. I'm Strong programming skills, particularly in the language Python.




How to Cite

A. A. Nafea, M. . Mishlish, A. M. S. Shaban, M. M. . AL-Ani, K. M. A. . Alheeti, and H. J. . Mohammed, “Enhancing Student’s Performance Classification Using Ensemble Modeling”, Iraqi Journal For Computer Science and Mathematics, vol. 4, no. 4, pp. 204–214, Nov. 2023.
DOI: 10.52866/ijcsm.2023.04.04.016
Published: 2023-11-19