[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118797-en":3,"doc-seo-118797-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118797,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Comprehensive Evaluations of Student Performance Estimation via Machine Learning","Student learning success is treated as the core objective of education, motivating automated methods for assessing learners early. This study applies multiple machine learning techniques to estimate early student performance using two evaluation simulations: traditional machine learning classifiers on the House dataset and convolutional neural networks evaluated across five datasets (House, WOU, XAPI, UCI, and Analytics Vidhya). Results show strong performance from an MLP baseline and even higher accuracies from the proposed CNN approach, which surpasses conventional methods and other reported state-of-the-art work under the same evaluation protocol.","Mohammad, Ahmad Saeed ORCID:  \n[https://orcid.org/0000-0001-6141-2605](https://orcid.org/0000-0001-6141-2605) , Al-Kaltakchi, Musab T. S. ORCID: [https://orcid.org/0000-0001-5542-9144](https://orcid.org/0000-0001-5542-9144) , Alshehabi Al-Ani, Jabir ORCID: [https://orcid.org/0000-0002-0553-2538 and](https://orcid.org/0000-0002-0553-2538 and)[ ](https://orcid.org/0000-0002-0553-2538 and)Chambers, Jonathon A. ORCID: [https://orcid.org/0000-0002-5820-](https://orcid.org/0000-0002-5820-)[ ](https://orcid.org/0000-0002-5820-)6509 (2023) Comprehensive Evaluations of Student Performance Estimation via Machine Learning. Mathematics, 11 (14) . p. 3153.  \nDownloaded from: [http://ray.yorksj.ac.uk/id/eprint/8304/](http://ray.yorksj.ac.uk/id/eprint/8304/)  \nThe version presented here may differ from the published version or version of record. If you intend to cite from the work you are advised to consult the publisher's version: [https://www.mdpi.com/2227-7390/11/14/3153](https://www.mdpi.com/2227-7390/11/14/3153)  \nResearch at York St John (RaY) is an institutional repository. It supports the principles of open access by making the research outputs of the University available in digital form. Copyright of the items stored in RaY reside with the authors and/or other copyright owners. Users may access full text items free of charge, and may download a copy for private study or non-commercial research. For further reuse terms, see licence terms governing individual outputs. Institutional Repository Policy Statement  \nRaY  \nResearch at the University of York St John  \nFor more information please contact RaY at [ray@yorksj.ac.uk](ray@yorksj.ac.uk)  \n 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","cbCaidp4rMPtX6dG","https://ap.wps.com/l/cbCaidp4rMPtX6dG","pdf",782815,1,17,"English","en",105,"# Introduction\n# Methodology and Evaluation\n## Traditional Machine Learning Classifiers\n## CNN Experiments and Datasets\n# Results and Discussion\n# Conclusion","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper focuses on predicting and estimating student performance early to help institutions assess strengths and shortcomings and support improvement during exams.\"},{\"question\":\"How is the evaluation conducted?\",\"answer\":\"Two simulations are used: traditional machine learning classifiers evaluated on the House dataset, and a CNN evaluated on multiple datasets (House, WOU, XAPI, UCI, and Analytics Vidhya) under the same protocol.\"},{\"question\":\"Which approach achieves the best reported performance?\",\"answer\":\"The CNN-based method delivers the highest accuracies across the evaluated datasets and is reported to surpass all seven conventional methods and other state-of-the-art work.\"}]","Comprehensive Evaluations of Student Performance Estimation via Machine Learning | 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