[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126276-en":3,"doc-seo-126276-105":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},126276,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Review and Analysis of Application of Improved Machine Learning Algorithms in Prediction of Students Academic Performance","Higher education access underpins economic growth, social justice, and academic success, yet student dropout remains a widespread obstacle for institutions worldwide. This study addresses early identification of at-risk learners by predicting whether students will thrive or discontinue using improved machine learning methods. The dataset contains 35 demographic, socioeconomic, academic, social, and macroeconomic attributes, including attendance, prior qualifications, nationality, and curricular performance. Data are preprocessed with Standard Scaler, feature correlation filtering, and IQR-based outlier removal, followed by grid-search hyperparameter tuning. Among six classifiers (SVM, Decision Tree, Random Forest, Naive Bayes, KNN, Logistic Regression), SVM achieves the strongest overall metrics, while Random Forest and Logistic Regression also show strong effectiveness for attrition prediction.","Review and Analysis of Application of Improved Machine Learning Algorithms in Prediction of Students Academic Performance  \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   \nAccess to higher education is essential for economic growth, social justice, and academic success. However, dropout rates area major issue for educational institutions worldwide. Socioeconomic position is one of several factors that contribute to the wide range in dropout rates between countries. Early identification of at-risk students is necessary to increase retention ratesand carry out successful treatments. This study predicts whether students will thrive academically or drop out using a variety of machine learning techniques. From several years we evaluated demographic, socioeconomic, academic, social, and macroeconomic aspects of students in different majors. Marital status, application mode, course, attendance type, prior qualifications, nationality, parental qualifications and occupations, special educational needs, gender, scholarship status, age at enrolment, debt status, tuition fee status, and curricular unit performance are among the 35 attributes that are included in the dataset. In order to pre-process the data, pertinent classes and attributes were found, negative correlations were removed from features, and outliers were found and eliminated using the Interquartile Range (IQR) method. We separated the dataset into a training set, which made up 67% of the total, and a testing set, which made up the remaining 33%, after normalizing it using Standard Scaler. The hyperparameters were optimized via grid search. Prediction models were developed using the following six classification algorithms: SVM, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbours (KNN), and Logistic Regression. It was discovered that the SVM model had the best F1-score, recall, accuracy, and precision. Random Forest and Logistic Regression outperformed Naive Bayes, KNN, and Decision Tree. The findings show that Random Forest, SVM, and Logistic Regression are effective models for predicting when students will leave school. By providing schools with effective tools for early risk assessment and customized intervention strategies, this study emphasizes the value of machine learning in enhancing educational administration and enhancing student accomplishment.  \nKeywords: Academic Performance, Student Attrition, Machine Learning, and Higher Learning Prediction Models  \n1. INTRODUCTION  \nHigher education serves as a cornerstone for individual advancement, social equity, and national development. Universities and colleges not only transmit knowledge but also play a vital role in shaping societal values, fostering cultural growth, advancing scientific research, and preparing individuals for the future workforce. Graduates of higher education institutions often emerge as critical thinkers and skilled professionals, capable of contributing meaningfully across diverse sectors of the economy. Moreover, higher education institutions frequently act as hubs for innovation and research, fostering advancementsin technology, medicine, economics, and the arts. Beyond personal success, the contributions of higher education reverberate across society. A well-educated populace strengthens the democratic fabric, drives technological  \nprogress, and fuels economic competitiveness. Countries with higher tertiary education enrollment typically enjoy better standards of living, lower unemployment rates, and greater civic participation. However, despite its transformative potential, higher education systems worldwide face a persistent challenge: s","cbCaioRJyQMuLGHh","https://ap.wps.com/l/cbCaioRJyQMuLGHh","pdf",493127,5,1,"English","en",105,"# Introduction\n## Higher Education and Dropout Challenge\n## Student Attrition Impacts\n## Motivation for Predictive Modeling\n# Methodology\n## Dataset Attributes and Evaluation Factors\n## Data Preprocessing and Feature Selection\n## Training/Testing Setup and Normalization\n## Hyperparameter Optimization\n## Classification Models and Comparative Results","[{\"question\":\"What problem does the study focus on?\",\"answer\":\"The study targets student dropout in higher education by predicting whether students will succeed academically or leave before completing their degrees.\"},{\"question\":\"What data processing steps are applied before modeling?\",\"answer\":\"The data are normalized with Standard Scaler, correlations with negative impact are removed, and outliers are detected and eliminated using the IQR method.\"},{\"question\":\"Which machine learning algorithms performed best for prediction?\",\"answer\":\"SVM achieved the best F1-score, recall, accuracy, and precision, while Random Forest and Logistic Regression also performed better than Naive Bayes, KNN, and Decision Tree.\"}]","Review and Analysis of Application of Improved Machine Learning Algorithms in Prediction of Students Academic Performance | 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problem does the study focus on?","Question",{"text":76,"@type":77},"The study targets student dropout in higher education by predicting whether students will succeed academically or leave before completing their degrees.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data processing steps are applied before modeling?",{"text":81,"@type":77},"The data are normalized with Standard Scaler, correlations with negative impact are removed, and outliers are detected and eliminated using the IQR method.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning algorithms performed best for prediction?",{"text":85,"@type":77},"SVM achieved the best F1-score, recall, accuracy, and precision, while Random Forest and Logistic Regression also performed better than Naive Bayes, KNN, and Decision 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