[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121200-en":3,"doc-seo-121200-105":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},121200,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","The Effects of Imbalanced Datasets on Machine Learning Algorithms in Predicting Student Performance","Predictive analytics increasingly supports higher-education decision-making, with student grades serving as a key indicator of academic achievement. This study analyzes how imbalanced datasets influence machine learning models’ accuracy and reliability for student performance prediction. Logistic Regression and Random Forest are compared using performance metrics and generalization capabilities under varying imbalance settings. Results show Random Forest reaches 98% accuracy versus 91% for Logistic Regression, and both models degrade under imbalance, with minority-class prediction particularly affected.","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage : www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nThe Effects of Imbalanced Datasets on Machine Learning Algorithms  \nin Predicting Student Performance  \nKhaled Mahmud Sujon a, Rohayanti Hassan a,*, Alif Ridzuan Khairudina , Sim Hiew Moia, Muhammad Luqman Mohd Shafiea, Zainuri Saringatb, Aldo Eriandac  \na Software Engineering Research Group, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor, Malaysia b Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia c Department of Information Technology, Politeknik Negeri Padang, Padang, Indonesia  \nCorresponding author:*[rohayanti@utm.my](rohayanti@utm.my)  \nAbstract—Predictive analytics technologies are becoming increasingly popular in higher education institutions. Students' grades are one of the most critical performance indicators educators can use to predict their academic achievement. Academics have developed numerous techniques and machine-learning approaches for predicting student grades over the last several decades. Although much work has been done, a practical model is still lacking, mainly when dealing with imbalanced datasets. This study examines the impact of imbalanced datasets on machine learning models' accuracy and reliability in predicting student performance. This study compares the performance of two popular machine learning algorithms, Logistic Regression and Random Forest, in predicting student grades. Secondly, the study examines the impact of imbalanced datasets on these algorithms' performance metrics and generalization capabilities. Results indicate that the Random Forest (RF) algorithm, with an accuracy of 98%, outperforms Logistic Regression (LR), which achieved 91% accuracy. Furthermore, the performance of both models is significantly impacted by imbalanced datasets. In particular, LR struggles to accurately predict minor classes, while RF also faces difficulties, though to a lesser extent. Addressing class imbalance is crucial, notably affecting model bias and prediction accuracy. This is especially important for higher education institutes aiming to enhance the accuracy of student grade predictions, emphasizing the need for balanced datasets to achieve robust predictive models.  \nKeywords—Imbalanced dataset; machine learning; higher education institute; multi-class prediction.  \nManuscript received 11 Mar. 2024; revised 17 Jun. 2024; accepted 22 Sep. 2024. Date of publication 30 Nov. 2024.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nHigher education institutions are now prioritizing the use of predictive analytics applications. Predictive analytics incorporates advanced analytics, including machine learning implementation, to extract valuable data and achieve highquality performance across all educational levels. A teacher's grade is one of the most critical performance indicators that can be used to monitor their students' academic progress [1]. Students' performance fluctuates throughout the year, and because of a decrease in overall performance caused by a variety of circumstances, eventually, the percentage of students who fail increases [2]. Data mining techniques make it possible to predict students' failures in the courses at an early stage. Consequently, we recognize that predicting student grades could be an effective strategy for raising student academic achievement. All higher education  \ninstitutions have grade-keeping systems and data management systems through which they collect information about their students. The educators would be able to anticipate the results of their students early based on previous results, which would be a revolutionary system. They can make many decisions to improve their students' performance levels. The use of predi","cbCainPoryjfPdem","https://ap.wps.com/l/cbCainPoryjfPdem","pdf",3814440,1,7,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Predictive analytics in higher education\n## Motivation and problem statement\n## Evaluation focus and expected impact","[{\"question\":\"Which machine learning algorithms are compared for predicting student grades?\",\"answer\":\"The study compares Logistic Regression and Random Forest for predicting student grades and evaluates their behavior under imbalanced data conditions.\"},{\"question\":\"How does class imbalance affect model performance in this study?\",\"answer\":\"Imbalanced datasets significantly reduce both models’ performance and generalization. Logistic Regression struggles more with minority classes, while Random Forest is also affected but to a lesser extent.\"},{\"question\":\"What accuracy results does the study report for Random Forest and Logistic Regression?\",\"answer\":\"Random Forest achieves about 98% accuracy, outperforming Logistic Regression, which achieves about 91% accuracy in the reported experiments.\"}]","The Effects of Imbalanced Datasets on Machine Learning Algorithms in Predicting Student Performance | PDF",1785734321,18,{"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},"the-effects-of-imbalanced-datasets-on-machine-learning-algorithms-in-predicting-student-performance","",{"@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/the-effects-of-imbalanced-datasets-on-machine-learning-algorithms-in-predicting-student-performance/121200/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are compared for predicting student grades?","Question",{"text":75,"@type":76},"The study compares Logistic Regression and Random Forest for predicting student grades and evaluates their behavior under imbalanced data conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does class imbalance affect model performance in this study?",{"text":80,"@type":76},"Imbalanced datasets significantly reduce both models’ performance and generalization. Logistic Regression struggles more with minority classes, while Random Forest is also affected but to a lesser extent.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy results does the study report for Random Forest and Logistic Regression?",{"text":84,"@type":76},"Random Forest achieves about 98% accuracy, outperforming Logistic Regression, which achieves about 91% accuracy in the reported experiments.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]