[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126275-en":3,"doc-seo-126275-105":30,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126275,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Design and Analysis of Students Academic Performance Prediction System Using Improved Machine Learning Methodologies - Research overview","Academic achievement, social justice, and economic progress depend on access to higher education, yet student dropout remains a persistent challenge globally. The study applies machine learning to predict academic success versus dropout by analyzing demographic, socioeconomic, academic, social, and macroeconomic characteristics across distinct majors. A dataset with 35 attributes is preprocessed using IQR-based outlier removal, removal of negative correlations, and Standard Scaler normalization. Models are tuned via grid search, and experiments compare SVM, Decision Tree, Random Forest, Naive Bayes, KNN, and Logistic Regression, with SVM showing the strongest classification performance.","Design and Analysis of Students Academic Performance Prediction System Using Improved Machine Learning Methodologies  \nShital Verma 1and Suvidya Sinha2  \n1 Research Scholar, Department of Mathematics, Faculty of Science, Patliputra University, Patna  \n2 Professor, Department of Mathematics, Faculty of Science, Patliputra University, Patna  \n Email: [shital.rwc@gmail.com](shital.rwc@gmail.com1)[1](shital.rwc@gmail.com1), [sinhasuvidya@gmail.com](sinhasuvidya@gmail.com2)[2](sinhasuvidya@gmail.com2)   \n Abstract   \nAcademic achievement, social justice, and economic progress all depend on having access to higher education. But dropout rates are a big problem for schools all throughout the world. A number of factors, including socioeconomic status, contribute to the large variation in dropout rates among nations. To improve retention rates and implement effective interventions, at-risk students must be identified early. This study uses a range of machine learning techniques to predict whether students will succeed academically or drop out. We assessed the demographic, socioeconomic, academic, social, and macroeconomic characteristics of students enrolled in distinct majors The dataset includes 35 attributes, including special educational needs, gender, scholarship status, age at enrolment, debt status, tuition fee status, marital status, application mode, course, attendance type, prior qualifications, nationality, parental qualifications and occupations, and curricular unit performance. The data was pre-processed by identifying relevant classes and attributes, eliminating outliers using the Interquartile Range (IQR) method, and removing negative correlations from features. After normalizing the dataset using Standard Scaler, we divided it into two sets: a training set, which accounted for 67% of the total, and a testing set, which included the remaining 33% . Grid search was used to optimize the hyperparameters. Six classification algorithms—SVM, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), and Logistic Regression—were used to create prediction models. The SVM model was shown to have the best accuracy, precision, recall, and F1-score. Compared to Naive Bayes, KNN, and Decision Trees, Random Forest and Logistic Regression performed better. The results demonstrate the efficacy of Random Forest, SVM, and Logistic Regression models in forecasting students' school departure times. This study highlights the importance of machine learning in improving educational administration and raising student achievement by giving schools useful tools for early risk assessment and tailored intervention tactics.  \nKeywords: Machine Learning, Higher Learning Prediction Models, Student Attrition, and Academic Performance  \n1. INTRODUCTION  \nThe foundation of social justice, personal growth, and national development is higher education. In addition to disseminating knowledge, universities and colleges are essential for establishing cultural development, developing scientific research, forming societal values, and preparing students for the workforce of the future. Higher education graduates frequently become knowledgeable professionals with critical thinking skills who can make significant contributions to a variety of economic areas. Furthermore, universities are often centers of research and invention, promoting breakthroughs in the fields of economics, medicine, technology, and the arts. The benefits of higher education extend beyond individual achievement and have an impact on the entire community. A population with a high level of education promotes economic competitiveness, advances technology, and fortifies the democratic fabric. improved tertiary education enrollment  \nis generally associated with improved living standards, reduced unemployment rates, and increased civic engagement in a nation. Student dropout is a recurring problem in higher education institutions around the world, despite its transformational potential.","cbCaioVIvIESM5Gg","https://ap.wps.com/l/cbCaioVIvIESM5Gg","pdf",429787,11,1,"English","en",105,"# Introduction\n## Higher education value and social impact\n## Definition and consequences of student dropout\n## Factors influencing dropout\n# Methodology\n## Dataset and feature set\n## Preprocessing: outlier removal and correlation handling\n## Normalization and train-test split\n## Hyperparameter optimization\n# Experimental Results\n## Classification algorithms compared\n## Performance metrics and best model\n# Discussion and Implications\n## Early risk assessment and intervention support","[{\"question\":\"What problem does the study address in higher education?\",\"answer\":\"The study addresses student dropout, focusing on identifying at-risk students early to improve retention and academic outcomes.\"},{\"question\":\"Which data features are used to build the prediction system?\",\"answer\":\"The dataset includes 35 attributes spanning special educational needs, gender, scholarship status, age at enrollment, debt and tuition-fee status, marital status, application mode, course, attendance type, prior qualifications, nationality, parental qualifications and occupations, and curricular unit performance.\"},{\"question\":\"Which machine learning models are evaluated and which performs best?\",\"answer\":\"The study evaluates SVM, Decision Tree, Random Forest, Naive Bayes, KNN, and Logistic Regression, with SVM achieving the best accuracy, precision, recall, and F1-score.\"}]","Design and Analysis of Students Academic Performance Prediction System Using Improved Machine Learning Methodologies - Research overview | PDF",1785904205,20,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"design-and-analysis-of-students-academic-performance-prediction-system-using-improved-machine-learning-methodologies-research-overview","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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/design-and-analysis-of-students-academic-performance-prediction-system-using-improved-machine-learning-methodologies-research-overview/126275/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address in higher education?","Question",{"text":76,"@type":77},"The study addresses student dropout, focusing on identifying at-risk students early to improve retention and academic outcomes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data features are used to build the prediction system?",{"text":81,"@type":77},"The dataset includes 35 attributes spanning special educational needs, gender, scholarship status, age at enrollment, debt and tuition-fee status, marital status, application mode, course, attendance type, prior qualifications, nationality, parental qualifications and occupations, and curricular unit performance.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models are evaluated and which performs best?",{"text":85,"@type":77},"The study evaluates SVM, Decision Tree, Random Forest, Naive Bayes, KNN, and Logistic Regression, with SVM achieving the best accuracy, precision, recall, and F1-score.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":29,"slug":127},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":29,"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":107,"slug":138},19,"General","general"]