[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122277-en":3,"doc-seo-122277-105":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},122277,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Predicting Student Dropout Risk using Machine Learning","Student dropout remains a persistent challenge in higher education, undermining institutional performance, reducing workforce preparedness, and limiting students’ academic and economic opportunities. Accurately identifying students at risk is complex due to academic, financial, and behavioral interactions. This thesis applies a combined machine learning framework integrating unsupervised and supervised techniques to predict dropout from structured first-year academic and financial data.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \n5-2025  \nPredicting Student Dropout Risk using Machine Learning  \nFatma Alameri[fka5462@rit.edu](fka5462@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nAlameri, Fatma, \"Predicting Student Dropout Risk using Machine Learning\" (2025) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Thesis is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nPredicting Student Dropout Risk using Machine  \nLearning  \nby  \nFatma Alameri  \nA Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Professional Studies: Data Analytics  \nDepartment of Graduate Programs & Research Rochester Institute of Technology, Dubai  \nMay 2025  \nMaster of Science in Professional Studies: Data Analytics  \nGraduate Thesis Approval  \nStudent Name: Fatma Alameri  \nThesis Title: Predicting Student Dropout Risk using Machine Learning  \nGraduate Committee  \nName: Dr. Sanjay Modak Date:  \nChair of Committee  \nName: Dr. Ioannis Karamitsos Date:  \nMember of Committee  \nAbstract  \nStudent dropout remains a persistent challenge in higher education, undermining institutional performance, reducing workforce preparedness, and limiting students’ academic and economic opportunities. Accurately identifying students at risk of attrition is complex, due to the interplay of academic, financial, and behavioral factors. This thesis addresses this challenge by applying a combined machine learning framework—integrating both unsupervised and supervised techniques—to predict student dropout using structured, first-year academic and financial data.  \nThe study utilizes a comprehensive dataset of 4,424 undergraduate student records from a European higher education institution, covering ten academic years and comprising 35 variables related to academic performance, enrollment behavior, and financial engagement. Clustering techniques were employed to group students by engagement profiles, while classification models—including Random Forest, XGBoost, and a soft voting ensemble—were trained to predict final academic outcomes: Dropout, Enrolled, or Graduate. Feature engineering was conducted in two phases, with both semester-averaged metrics and advanced derived indicators used to enhance model performance.  \nFindings show that academic approvals, grades, and tuition fee status are the most influential predictors of student outcomes. Unsupervised clustering revealed behaviorally distinct groups with statistically significant dropout risks, though these clusters did not translate effectively into predictive labels. Supervised models, particularly tuned XGBoost and ensemble classifiers, achieved high performance in binary classification tasks (balanced accuracy ¿ 0.91, AUC ¿ 0.95), confirming that dropout risk can be reliably predicted from early academic records. However, multiclass classification performance declined, especially for the transitional “Enrolled” category, highlighting the limitations of static early-year data in capturing more ambiguous student states.  \nThis research contributes to the literature by demonstrating the strengths and constraints of interpretable machine learning in modeling student success. It also offers actionable insights for academic institutions, such as prioritizing interventions for students with early signs of disengagement and financial instability. Methodologically, the study highlights opportunities for future work to explore hybrid clustering-classification models, apply soft clustering techniques, and evaluate deep learning models for benchmarking purposes. While complex models may lack interpretability, they can serve as useful baselines to understand performance ceilings  \nwithin structured educational datasets.  \nKeywords: Stu","cbCaiic63tiMNx27","https://ap.wps.com/l/cbCaiic63tiMNx27","pdf",2545686,1,60,"English","en",105,"# Abstract\n# List of Figures\n# List of Tables\n# Introduction\n## Background\n## Problem Statement\n## Research Aim and Objectives\n## Research Questions\n# Literature Review\n## Key Predictors of Student Dropout\n## Machine Learning Models for Dropout Prediction\n## Use of Unsupervised Learning in Dropout Prediction\n## Interpretability and Feature Importance in Dropout Prediction\n## Temporal and Contextual Factors Affecting Dropout","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis focuses on predicting student dropout risk in higher education using early, structured first-year academic and financial data.\"},{\"question\":\"How does the study build its predictive models?\",\"answer\":\"It combines unsupervised clustering with supervised classification models, including Random Forest, XGBoost, and a soft voting ensemble, after two phases of feature engineering.\"},{\"question\":\"Which factors most influence student outcomes?\",\"answer\":\"The study finds that academic approvals, grades, and tuition fee status are the most influential predictors of students’ final outcomes.\"}]","Predicting Student Dropout Risk using Machine Learning | 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