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The study applies multiple machine learning techniques to estimate early student performance through two simulation settings. The first evaluates traditional machine learning classifiers on the House dataset, while the second uses a convolutional neural network across several datasets and dataset subdivisions. Results indicate the proposed CNN method achieves higher accuracy than conventional approaches under the same evaluation protocol.","mathematics  \nArticle  \nComprehensive Evaluations of Student Performance Estimation via Machine Learning  \nAhmad Saeed Mohammad 1, Musab T. S. Al-Kaltakchi 2, Jabir Alshehabi Al-Ani 3, *  \nand Jonathon A. Chambers 4  \nCitation: Mohammad, A.S.;  \nAl-Kaltakchi, M.T.S.;  \nAlshehabi Al-Ani, J.; Chambers, J.A. Comprehensive Evaluations of Student Performance Estimation via Machine Learning. Mathematics 2023, 11, 3153. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/math11143153](10.3390/math11143153)  \nAcademic Editor: Zhao Kang  \nReceived: 11 June 2023  \nRevised: 11 July 2023  \nAccepted: 17 July 2023  \nPublished: 18 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Engineering, College of Engineering, Mustansiriyah University, Baghdad 10047, Iraq; [ahmad.saeed@uomustansiriyah.edu.iq](ahmad.saeed@uomustansiriyah.edu.iq)  \n2 Department of Electrical Engineering, College of Engineering, Mustansiriyah University, Baghdad 10047, Iraq; [m.t.s.al_kaltakchi@uomustansiriyah.edu.iq](m.t.s.al_kaltakchi@uomustansiriyah.edu.iq)  \n3 Data Science Department, York St. John University, York YO31 7EX, UK  \n4 Communications, Sensors, Signal and Information Processing (ComS2 IP) Group, School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; [jonathon.chambers@ncl.ac.uk](jonathon.chambers@ncl.ac.uk)  \n* [Correspondence: j.alshehabialani@yorksj.ac.uk or j.alshehabial-ani1@lancaster.ac.uk](Correspondence: j.alshehabialani@yorksj.ac.uk or j.alshehabial-ani1@lancaster.ac.uk)  \nAbstract: Success in student learning is the primary aim of the educational system. Artificial intelligence utilizes data and machine learning to achieve excellence in student learning. In this paper, we exploit several machine learning techniques to estimate early student performance. Two main simulations are used for the evaluation. The first simulation used the Traditional Machine Learning Classifiers (TMLCs) applied to the House dataset, and they are Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), Random Forest (RF), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA) . The best results were achieved with the MLP classifier with a division of 80% training and 20% testing, with an accuracy of 88.89% . The fusion of these seven classifiers was also applied and the highest result was equal to the MLP. Moreover, in the second simulation, the Convolutional Neural Network (CNN) was utilized and evaluated on five main datasets, namely, House, Western Ontario University (WOU), Experience Application Programming Interface (XAPI), University of California-Irvine (UCI), and Analytics Vidhya (AV) . The UCI dataset was subdivided into three datasets, namely, UCI-Math, UCI-Por, and UCI-Fused. Moreover, the AV dataset has three targets which are Math, Reading, and Writing. The best accuracy results were achieved at 97.5%, 99.55%, 98.57%, 99.28%, 99.40%, 99.67%, 92.93%, 96.99%, and 96.84% for the House, WOU, XAPI, UCI-Math, UCI-Por, UCI-Fused, AV-Math, AV-Reading, and AV-Writing datasets, respectively, under the same protocol of evaluation. The system demonstrates that the proposed CNN-based method surpasses all seven conventional methods and other state-of-the-art-work.  \nKeywords: student performance; machine learning; Gaussian Naïve Bayes; support vector machine; decision tree; multi-layer perceptron; random forest; linear discriminant analysis; quadratic discriminant analysis; convolutional neural network  \nMSC: 68T07  \n1. Introduction  \nOne of the main issues facing not only the majority of schools, colleg","cbCaijvClKWrtFPg","https://ap.wps.com/l/cbCaijvClKWrtFPg","pdf",724338,1,16,"English","en",105,"# Introduction\n## Problem setting in student performance prediction\n## Role of learning management systems and data analysis\n## Machine learning approaches for early evaluation\n## Related work: systematic literature reviews","[{\"question\":\"What is the paper trying to achieve in student learning assessment?\",\"answer\":\"It estimates early student performance using machine learning so institutions can identify strengths and weaknesses and improve outcomes during exams.\"},{\"question\":\"How is the evaluation carried out in the study?\",\"answer\":\"Two simulations are used: one with traditional machine learning classifiers on the House dataset, and another with a convolutional neural network evaluated on multiple datasets under the same protocol.\"},{\"question\":\"Which model type shows the best performance and what does it outperform?\",\"answer\":\"The CNN-based method yields the highest reported accuracy values and surpasses the seven conventional methods and other state-of-the-art work mentioned in the abstract.\"}]","Comprehensive Evaluations of Student Performance Estimation via Machine Learning | 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is the paper trying to achieve in student learning assessment?","Question",{"text":75,"@type":76},"It estimates early student performance using machine learning so institutions can identify strengths and weaknesses and improve outcomes during exams.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the evaluation carried out in the study?",{"text":80,"@type":76},"Two simulations are used: one with traditional machine learning classifiers on the House dataset, and another with a convolutional neural network evaluated on multiple datasets under the same protocol.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model type shows the best performance and what does it outperform?",{"text":84,"@type":76},"The CNN-based method yields the highest reported accuracy values and surpasses the seven conventional methods and other state-of-the-art work mentioned in the 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