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This study analyzed how machine learning and data can transform education in Uganda. Using insights from Eccles and Roeser (2015) and a literature review, it addressed withdrawal before completion in primary and secondary schooling. A random forest model was employed to predict dropout rates and identify at-risk students, then proposed a framework spanning data collection, model development, and stakeholder commitment.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/tackling-dropout-rates-of-students-in-uganda-an-exploration-of-machine-learning-and-data-driven-approaches/290551/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/tackling-dropout-rates-of-students-in-uganda-an-exploration-of-machine-learning-and-data-driven-approaches/290551.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the dissertation address?","Question",{"text":112,"@type":113},"The dissertation addresses high dropout rates of students in Uganda and their impact on student outcomes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning method is used in the study?",{"text":117,"@type":113},"A random forest machine learning algorithm is used to predict student dropout rates and identify at-risk students.",{"name":119,"@type":110,"acceptedAnswer":120},"What framework does the study propose to tackle dropout rates?",{"text":121,"@type":113},"The framework includes data collection, model development, and stakeholder commitment to support data-driven action in education.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290551,1789642053,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","TACKLING DROPOUT RATES OF STUDENTS IN UGANDA: AN EXPLORATION OF MACHINE LEARNING AND DATA-DRIVEN APPROACHES  \nDIANA KIRABO NAKIMBUGWE  \nM23M19/278  \nA DISSERTATION SUBMITTED TO THE FACULTY OF ENGINEERING, DESIGN AND TECHNOLOGY IN PARTIAL FULFILLMENT FOR THE REQUIREMENTS OF THE AWARD OF THE DEGREE OF MASTER OF SCIENCE IN DATA SCIENCE AND ANALYTICS OF  \nUGANDA CHRISTIAN UNIVERSITY  \nSeptember,2025  \nABSTRACT  \nUganda’s education sector faced notable challenges, including high dropout rates and poor student outcomes. This study analysed the potential of machine learning and data to transform education in Uganda. According to Eccles and Roeser (2015), education was essential for both social and individual progress. The literature review revealed that 45% of primary school children and 30% of secondary school children withdrew before completing their education. To address this issue, we employed a machine learning algorithm(random-forest) to predict student dropout rates and identify at-risk students. Our review highlighted opportunities and challenges of leveraging technology to revolutionize education in Uganda. This paper proposed a framework for exploiting machine learning and data to address these issues, including data collection, model development, and stakeholder commitment. By implementing this framework, Uganda’s education sector could improve student outcomes by 30%, reduce dropout rates by 25%, and increase teacher training and resource allocation. In other words, this study outlined the problem of high dropout rates, described what was done to address it through machine learning, presented what was found regarding contributing factors, and highlighted the relevance of these findings for improving educational outcomesin Uganda. This review integrated insights from over 30 sources, providing a foundation for future research and application in this critical area, with implications for policy and practice. Keywords Primary Keywords: Machine learning, Data analytics, Educational outcomes, Dropout rates, Student performance. Secondary Keywords: Predictive modeling, Random Forest algorithm, Data-driven decision-making, Educational data mining, Learning analytics, Ugandan education, East Africa, Educational technology and Descriptors (MeSH Terms):  \nEducation, Machine learning, Data analytics, Student dropouts, Academic achievement, Educational measurement, Educational technology, and Africa.  \nDECLARATION  \nI, Nakimbugwe Diana Kirabo, hereby declare that this is my original work, is not plagiarized, and has not been submitted to any other institution for any award.  \nStudent’s Name: Nakimbugwe Diana Kirabo  \nSignature:  \nDate: 25th /09/2025  \nAPPROVAL  \nThis is to certify that this research titled TACKLING DROPOUT RATES OF STUDENTS IN UGANDA: AN EXPLORATION OF MACHINE LEARNING AND  \nDATA-DRIVEN APPROACHES, has been done under my supervision and is now ready  \nfor submission.  \nSupervisor: Mr. Ian Raymond Osolo  \nSignature:   \nDate: 25th /09/2025  \nDEDICATION  \nI dedicate my research to my family, whose unwavering support and encouragement have been my guiding light throughout this journey, making me into the person I am today.  \nThank you.  \nACKNOWLEDGMENTS  \nI hereby express my appreciation and gratitude to the following individuals who contributed towards the completion of this Masterś thesis: My God for strength and wisdom, my advisor Mr. Ian Osolo for his guidance, the UCU research committee, and my family and friends.  \nTABLE OF CONTENTS  \nABSTRACT ........................................ ii  \nDECLARATION ...................................... iii  \nAPPROVAL ........................................ iv  \nDEDICATION ....................................... v  \n[ACKNOWLEDGMENTS ................................. vi](ACKNOWLEDGMENTS ................................. vi)  \n[LIST OF FIGURES .................................... ix](LIST OF FIGURES .................................... ix)  \n[LIST OF TABLES ........................","cbCailflLHOVw5Rl","https://ap.wps.com/l/cbCailflLHOVw5Rl","pdf",9393829,116,"English","# ABSTRACT\n# DECLARATION\n# APPROVAL\n# DEDICATION\n# ACKNOWLEDGMENTS\n# LIST OF FIGURES\n# LIST OF TABLES\n# 1 INTRODUCTION\n## 1.0.1 General Introduction\n## 1.0.2 Overview of the education sector\n## 1.0.3 Research problem and importance\n## 1.0.4 Main and specific objectives\n## 1.0.5 Research questions\n## 1.0.6 Hypothesis\n## 1.0.7 Methodology\n## 1.0.8 Theoretical framework and conceptual model\n## 1.0.9 Conceptual framework\n## 1.0.10 Thesis outline/map out of work\n# 2 REVIEW OF LITERATURE\n## 2.0.1 Introduction\n## 2.0.2 An overview of dropout rates in Uganda\n## 2.0.3 Theories associated to dropouts\n## 2.0.4 Factors influencing learner dropout\n## 2.0.5 Data for model development\n## 2.0.6 Machine learning approaches and student dropout\n## 2.0.7 Evaluation measures for students dropout\n## 2.0.8 The role of machine learning in droput prediction\n## 2.0.9 Challenges and limitations\n## 2.0.10 Contribution of this study to ML and data-driven approaches in Education\n## 2.0.11 Conclusion\n# 3 METHODOLOGY\n## 3.0.1 Introduction\n## 3.0.2 Research approach\n## 3.0.3 Philosophical paradigm\n## 3.0.4 Research design\n## 3.0.5 Study variables\n## 3.0.6 Sampling","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"The dissertation addresses high dropout rates of students in Uganda and their impact on student outcomes.\"},{\"question\":\"Which machine learning method is used in the study?\",\"answer\":\"A random forest machine learning algorithm is used to predict student dropout rates and identify at-risk students.\"},{\"question\":\"What framework does the study propose to tackle dropout rates?\",\"answer\":\"The framework includes data collection, model development, and stakeholder commitment to support data-driven action in education.\"}]","TACKLING DROPOUT RATES OF STUDENTS IN UGANDA - AN EXPLORATION OF MACHINE LEARNING AND DATA-DRIVEN APPROACHES | PDF",292]