[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126908-en":3,"doc-seo-126908-105":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":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},126908,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Applications of machine learning in operational aspects of academia - a review","Digital transformation and advanced machine learning (ML) enable higher-education institutions to accumulate extensive data and perform complex decision-making in ways previously impractical. Beyond teaching and research, ML reshapes university functions, particularly through improvements in administrative performance, operational efficiency, and streamlined processes. This review synthesizes existing knowledge on ML applications in non-teaching, operational domains of academia and outlines directions for future research. The reported impacts include reducing educators’ administrative load.","Applications of machine learning in operational aspects of  \nacademia: a review  \nMuhammad Nadeem, Wael Farag, Zekeriya Uykan, Magdy Helal  \nCollege of Engineering and Technology, American University of the Middle East, Egaila, Kuwait  \nArticle history:  \nReceived Nov 20, 2023 Revised Feb 28, 2024 Accepted Mar 10, 2024  \nKeywords:  \nAdministration operations Digital transformation Higher educational institutes Machine learning Operational aspects University administration  \nCorresponding Author:  \nEducational institutions, propelled by digital transformation and sophisticated machine learning (ML) algorithms, amass plentiful data, facilitating the execution of complicated decision-making tasks previously inconceivable. ML ’s pervasive influence extends beyond pedagogy and research, profoundly altering the fabric of academia and reshaping university functionalities. Its deployment in university administration enhances efficacy, efficiency, and operational streamlining across diverse levels. This article conducts a comprehensive review of extant knowledge pertaining to the diverse applications of ML in non-teaching domains within academic settings, delineating avenues for future research. The recognized findings furnish a robust foundation for the further exploration and refinement of ML applications, particularly within the administrative and operational realms of academia. A consequential outcome of this transformative integration is the mitigation of teachers ’ administrative burdens. In practical terms, this liberation affords educators the opportunity to redirect their time and energy towards their primary responsibilities of educating and fostering the intellectual development of their students.  \nThis is an open access article under the CC BY-SA license.  \nMuhammad Nadeem  \nCollege of Engineering and Technology, American University of the Middle East Egaila, 54200, Kuwait  \n[Email: muhammad.nadeem@aum.edu.kw](Email: muhammad.nadeem@aum.edu.kw)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe use of machine learning (ML) has become necessary in almost all fields of life. ML provides useful insights into the current education system that have not been seen before. The reason is the ML’s ability to convert data into intelligent actions. All educational institutions gather significant amounts of data, at all levels, usable for assessments and decision-making purposes. It is, therefore, natural to utilize ML for developing and obtaining more useful insights out of the available data, for the benefit of the students and the academicians as well as the educational management systems [1] . The world bank reckoned that the investments in artificial intelligence (AI) applications in education have crossed the figure of USD 1 billion from 2008 to 2019 [2], [3] . The significance of AI usability in education has been emphasized by UNESCO and the OECD, for sustainable development in society [4] . The market size of ML services is expected to grow beyond USD 300 billion by 2030 as shown in Figure 1. Its applications have exploded in various fields of study such as healthcare [5] and education [6], [7] .  \nMachine learning is a branch of AI that aims at developing models and algorithms that allow computers to learn from given datasets and become better at a given task without having to be explicitly programmed. It enables computers to draw conclusions or take actions based on what they have learned from past experiences or historical data. ML is a broad science with many subfields, each of which focuses on  \nfacets, methods, and applications. Some of the well-known ML subfields include deep learning, reinforcement learning, federated learning, unsupervised learning, and supervised learning.  \nFigure 1. Machine learning market size and expected growth by 2030 [8]  \nMachine learning techniques have become widely used in a variety of educational contexts because of their propensity to analyze huge datasets and spot patterns. This includes i","cbCaibYgJeAZ4RzM","https://ap.wps.com/l/cbCaibYgJeAZ4RzM","pdf",506301,1,21,"English","en",105,"# 1. Introduction\n# 2. Machine learning background\n# 3. ML in non-teaching operational aspects of academia\n# 4. Impacts on administration and educators\n# 5. Future research directions","[{\"question\":\"What problem does machine learning address in academia’s operational setting?\",\"answer\":\"ML converts large institutional datasets into actionable insights, supporting decision-making in administration and other non-teaching functions within academic settings.\"},{\"question\":\"Which university activities fall under the non-teaching operational aspects discussed in the review?\",\"answer\":\"The review focuses on administrative duties, student services, and research projects aimed at improving education quality—activities that support the educational ecosystem without being directly pedagogical.\"},{\"question\":\"How can ML-driven administrative integration affect lecturers?\",\"answer\":\"It can mitigate teachers’ administrative burdens, freeing time and energy to concentrate on teaching, mentoring, and student intellectual development.\"}]","Applications of machine learning in operational aspects of academia - a review | 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problem does machine learning address in academia’s operational setting?","Question",{"text":76,"@type":77},"ML converts large institutional datasets into actionable insights, supporting decision-making in administration and other non-teaching functions within academic settings.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which university activities fall under the non-teaching operational aspects discussed in the review?",{"text":81,"@type":77},"The review focuses on administrative duties, student services, and research projects aimed at improving education quality—activities that support the educational ecosystem without being directly pedagogical.",{"name":83,"@type":74,"acceptedAnswer":84},"How can ML-driven administrative integration affect lecturers?",{"text":85,"@type":77},"It can mitigate teachers’ administrative burdens, freeing time and energy to concentrate on teaching, mentoring, and student intellectual 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