[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122397-en":3,"doc-seo-122397-105":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},122397,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting and Forecasting University Rankings in the UAE using Machine Learning","Higher Education Institutions (HEIs) shape knowledge economies, while institutional rankings increasingly act as global benchmarks of academic performance and reputation. In the United Arab Emirates (UAE), improving competitive position in international rankings supports national strategies such as UAE Vision 2030 and the Centennial Plan 2071. This research develops machine learning predictive models to forecast institutional ranking outcomes and enable data-driven planning and continuous academic improvement. Using a dataset from QS World University Rankings (2020–2024) cross-referenced with CAA listings, it builds models with preprocessing, feature selection, and evaluation via correlation, MSLE, and feature importance.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \n5-2025  \nPredicting and Forecasting University Rankings in the UAE using Machine Learning  \nFatma Ibrahim Ahmad [fia4811@rit.edu](fia4811@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nAhmad, Fatma Ibrahim, \"Predicting and Forecasting University Rankings in the UAE 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 and Forecasting University Rankings in the UAE  \nusing Machine Learning  \nBy  \nFatma Ibrahim Ahmad  \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  \nRochester Institute of Technology  \nRIT Dubai  \nMay 2025  \nMaster of Science in Professional Studies: Data Analytics  \nGraduate Thesis Approval  \nStudent Name: Fatma Ibrahim Ahmad  \nThesis Title: Predicting and Forecasting University Rankings in the UAE using Machine Learning  \nGraduate Thesis Committee:  \nName: Dr. Sanjay Modak  \nChair of committee  \nDate:  \nName: Dr. Hammou Messatfa  \nMember of committee  \nDate:  \nAcknowldgement  \nFirst, I would like to express my sincere and heartfelt appreciation to Dr. Hammou Messatfa for his exceptional mentorship and guidance throughout this academic journey. His thoughtful direction, academic integrity, and consistent support significantly shaped the quality and depth of this work. His role extended far beyond that of a mentor, he challenged my thinking, inspired confidence, and supported me at every step of the way.  \nI am also truly honored to extend my heartfelt appreciation to Her Excellency Sheikha Khulood Saqer Al Qasimi, the Assistant Undersecretary of the Inspection and Licensing Sector at the Ministry of Education. Her leadership, trust, and continued support have been both inspiring and empowering. Her thoughtful guidance played a pivotal role in the successful completion of my master’s degree.  \nI would like to sincerely thank my colleagues at the Inspection and Licensing Sector at the Ministry of Education, whose support and insights have left a meaningful and lasting impact on this thesis.  \nI also wish to acknowledge the Rochester Institute of Technology (RIT) and its distinguished faculty for fostering an environment of academic rigor and innovation. The resources, expertise, and support provided by the institution have been fundamental to the successful completion of this work. I am equally grateful to my peers and colleagues for their enriching discussions and collaborative spirit, which greatly enhanced my understanding and broadened the perspective of this study.  \nAbstract  \nHigher Education Institutions (HEIs) play a vital role in advancing knowledge economies, and institutional rankings are increasingly used as global benchmarks of academic performance and reputation. In the context of the United Arab Emirates (UAE), enhancing institutional competitiveness in global rankings aligns with national strategies such as UAE Vision 2030 and the Centennial Plan 2071. This research applies machine learning (ML) techniques to develop predictive models that forecast institutional ranking outcomes, enabling data-driven planning and continuous academic improvement.  \nThe study utilizes a dataset compiled from the QS World University Rankings (2020–2024), crossreferenced against the institutional listings maintained by the Commission for Academic Accreditation (CAA) to ensure alignment with the UAE’s accredited higher education landscape.  \nIt includes over 4,000 records covering performance metrics such as research output, facultystudent ratios, academic and employer reputation, international collaborat","cbCaiaKV0MQy00uZ","https://ap.wps.com/l/cbCaiaKV0MQy00uZ","pdf",5571236,1,100,"English","en",105,"# Abstract\n## Dataset and data preparation\n## Modeling approach and evaluation\n## Results and implications\n## Future research directions","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses how to enhance UAE university competitiveness in global rankings by forecasting institutional ranking outcomes using machine learning models.\"},{\"question\":\"What dataset is used for model development?\",\"answer\":\"The dataset combines QS World University Rankings (2020–2024) with institutional listings maintained by the Commission for Academic Accreditation (CAA) to match the UAE accredited higher education landscape.\"},{\"question\":\"Which machine learning methods are evaluated and what is the top performer?\",\"answer\":\"Five algorithms are implemented: XGBoost Tree, Neural Network, Linear Support Vector Machine (LSVM), Linear Regression, and Generalized Linear Model (GLM). XGBoost achieves the highest predictive performance with correlation reaching 95%.\"}]","Predicting and Forecasting University Rankings in the UAE using Machine Learning | PDF",1785810423,252,{"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},"predicting-and-forecasting-university-rankings-in-the-uae-using-machine-learning","",{"@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/predicting-and-forecasting-university-rankings-in-the-uae-using-machine-learning/122397/",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","2026-08-04",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},"What problem does the study address?","Question",{"text":75,"@type":76},"The study addresses how to enhance UAE university competitiveness in global rankings by forecasting institutional ranking outcomes using machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used for model development?",{"text":80,"@type":76},"The dataset combines QS World University Rankings (2020–2024) with institutional listings maintained by the Commission for Academic Accreditation (CAA) to match the UAE accredited higher education landscape.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods are evaluated and what is the top performer?",{"text":84,"@type":76},"Five algorithms are implemented: XGBoost Tree, Neural Network, Linear Support Vector Machine (LSVM), Linear Regression, and Generalized Linear Model (GLM). 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