Title: A comparative study of supervised/unsupervised machine learning algorithms with feature selection approaches to predict student performance

Authors: Alaa Khalaf Hamoud; Ali Salah Alasady; Wid Akeel Awadh; Jasim Mohammed Dahr; Mohammed B.M. Kamel; Aqeel Majeed Humadi; Ihab Ahmed Najm

Addresses: Department of Computer Information System, University of Basrah, Iraq ' Department of Computer Information System, University of Basrah, Iraq ' Department of Computer Information System, University of Basrah, Iraq ' Department of Computer Information System, University of Basrah, Iraq ' Department of Computer Information System, University of Basrah, Iraq ' Department of Computer Information System, University of Basrah, Iraq ' Department of Computer Information System, University of Basrah, Iraq

Abstract: The field of educational data mining (EDM) is one of the most growing fields that aims to improve the performance of students, academic staff, and overall institutional performance. The implementing process of data mining algorithms almost needs the feature selection process to find the most correlated features and improve the accuracy. In this paper, a comparative study is performed to study implementation of supervised/unsupervised algorithms in predicting the students' performance. The student's grade is classified using different fields of supervised and unsupervised algorithms such as decision trees, clustering, and neural networks. These algorithms were examined over the questionnaire dataset before/after feature selection to measure the effect of feature selection on the result accuracy. The results showed that the random forest decision tree outperformed other supervised/unsupervised algorithms. The results also showed that the performance evaluation of algorithms with the dataset after removing the less correlated attributes is enhanced for most of the algorithms.

Keywords: educational data mining; EDM; students' performance; supervised algorithms; unsupervised algorithms; feature selection.

DOI: 10.1504/IJDMMM.2023.134590

International Journal of Data Mining, Modelling and Management, 2023 Vol.15 No.4, pp.393 - 409

Received: 15 Jul 2022
Accepted: 26 Nov 2022
Published online: 30 Oct 2023 *

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