[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124081-en":3,"doc-seo-124081-105":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":20,"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},124081,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Intelligent System for Student Performance Prediction Using Machine Learning","Accurately predicting student performance remains a major challenge in education. Early identification of students who require extra support can improve academic outcomes and reduce failure rates. This study builds an intelligent supervised machine learning solution to overcome limitations of existing prediction models and raise prediction accuracy. Random Forest, Extra Trees, and K-Nearest Neighbors are trained and evaluated using 24,000 training and 6,000 testing instances, with preprocessing for optimization.","Intelligent System for Student Performance Prediction Using Machine Learning  \nMustafa S. Ibrahim Alsumaidaie1, Ahmed AdilNafea*2, Abdulrahman Abbas Mukhlif3  \n, Ruqaiya D. Jalal1,, Mohammed MAL-Ani4  \n1Department of Computer Science, College of Computer Science and IT, University of Anbar Ramadi, Iraq. 2Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Iraq.  \n3Registration and Students Affairs, University Headquarter, University of Anbar, Anbar, Iraq.  \n4Center for Artificial Intelligence Technology (CAIT)، Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.  \nReceived 24/09/2023, Revised 26/01/2024, Accepted 28/01/2024, Published Online First 20/05/2024, Published 05/11/2024  \n © 2022 The Author(s) . Published by College of Science for Women, University of Baghdad.  \nThis is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nAbstract  \nAccurately predicting student performance remains a significant challenge in the educational sector. Identifying students who need additional support early can significantly impact their academic outcomes. This study aims to develop an intelligent solution for predicting student performance using supervised machine learning algorithms. This proposed focus on addressing the limitations of existing prediction models and enhancing prediction accuracy. In this work employed three supervised machine learning algorithms: Random Forest, Extra Trees, and K-Nearest Neighbors. The steps of research methodology contained (data collection, preprocessing, feature identification, model construction, and evaluation) . This paper utilized a dataset comprising 24,000 training instances and 6,000 testing instances, applying various preprocessing techniques for data optimization. The Extra Trees algorithm achieved the highest accuracy (98.15%), followed by Random Forest (94.03%) and K-Nearest Neighbors (91.65%) . All algorithms demonstrated high precision and recall. Notably, K-Nearest Neighbors exhibited exceptional computational efficiency with a training time of 0.00 seconds. This study proposed an efficient model for prediction student performance. The high accuracy and efficiency of the proposed system highlight its potential for application in educational data mining. The findings of this proposed to improving student success rates in educational institutions by enabling timely and appropriate interventions.  \nKeywords: Artificial Intelligence, Educational Data Mining, Extra Trees Algorithm, Student Performance Prediction, Supervised Machine Learning.  \nIntroduction  \nStudent performance prediction is an important role in the field of education and the management of educational institutions. Understanding the importance of anticipating student performance helps educational institutions make strategic decisions and improve overall educational outcomes.  \nProviding an accurate prediction of student performance that educational institutions can benefit from in several aspects. It can help to identify students' needs and intervene early to meet those needs. When there have been expectations about student performance, could be effectively direct  \nefforts and resources to provide the support needed to the students who need it most 1. This can help reduce the failure rate and improve overall academic success. Student performance expectations can be used to improve the strategic planning process in educational institutions. Based on performance expectations, institutions can develop customized educational programs that target potential weaknesses and enhance students' academic capabilities. Tests and assessments may also be structured based on these expectations to ensure that educational goals are met, and students are mo","cbCaip9prpEw4hIX","https://ap.wps.com/l/cbCaip9prpEw4hIX","pdf",1308496,1,15,"English","en",105,"# Introduction\n## Motivation and importance of predicting performance\n## Limitations of traditional methods and research gap\n# Methodology\n## Data collection and preprocessing\n## Feature identification\n## Model construction and evaluation\n# Experimental Results\n## Model comparison and accuracy\n## Precision, recall, and efficiency\n# Conclusion\n## Proposed model impact on timely interventions","[{\"question\":\"Why is student performance prediction important in educational institutions?\",\"answer\":\"It helps institutions make strategic decisions, intervene early for students who need support, improve planning and program customization, and reduce failure rates. It also supports more effective teaching and assessment based on expected performance.\"},{\"question\":\"Which supervised machine learning algorithms are used in the proposed study?\",\"answer\":\"The study employs Random Forest, Extra Trees, and K-Nearest Neighbors. These models are trained using a dataset split into training and testing instances with preprocessing.\"},{\"question\":\"How does the Extra Trees algorithm perform compared with other models?\",\"answer\":\"Extra Trees achieves the highest reported accuracy at 98.15%. Random Forest follows at 94.03%, and K-Nearest Neighbors reaches 91.65%, with all algorithms showing high precision and recall.\"}]","Intelligent System for Student Performance Prediction Using Machine Learning | PDF",1785820234,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"intelligent-system-for-student-performance-prediction-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/intelligent-system-for-student-performance-prediction-using-machine-learning/124081/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is student performance prediction important in educational institutions?","Question",{"text":75,"@type":76},"It helps institutions make strategic decisions, intervene early for students who need support, improve planning and program customization, and reduce failure rates. It also supports more effective teaching and assessment based on expected performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which supervised machine learning algorithms are used in the proposed study?",{"text":80,"@type":76},"The study employs Random Forest, Extra Trees, and K-Nearest Neighbors. These models are trained using a dataset split into training and testing instances with preprocessing.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the Extra Trees algorithm perform compared with other models?",{"text":84,"@type":76},"Extra Trees achieves the highest reported accuracy at 98.15%. Random Forest follows at 94.03%, and K-Nearest Neighbors reaches 91.65%, with all algorithms showing high precision and recall.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]