[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119438-en":3,"doc-seo-119438-105":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":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},119438,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Evaluating Higher Education Performance via Machine Learning During Disruptive Times - A Case of Applied Education in Türkiye","The COVID-19 pandemic triggered rapid digitisation and a global shift to online higher education, leaving an evidence gap on how well anti-COVID measures preserved education quality. This study uses machine learning to analyze student grades as a proxy for educational standards and compares its evaluative potential with traditional statistics. The results highlight the analytical utility of machine learning when educational data are irregular and limited, while emphasizing that accurate, meaningful data points are essential for reliable performance.","European Journal of Education  \nORIGINAL ARTICLE  OPEN ACCESS   \nEvaluating Higher Education Performance via Machine Learning During Disruptive Times: A Case of Applied Education in Türkiye  \nSemih Sait Yılmaz1  | Ayşe Collins1 | Seyid Amjad Ali2  \n1Department of Tourism and Hotel Management, Faculty of Applied Sciences, I.D. Bilkent University, Ankara, Turkey | 2Department of Information  \nSystems and Technologies, Faculty of Applied Sciences, I.D. Bilkent University, Ankara, Turkey Correspondence: Semih Sait Yılmaz ([semihsaityilmaz@bilkent.edu.tr](semihsaityilmaz@bilkent.edu.tr))  \nReceived: 2 May 2024 | Revised: 23 September 2024 | Accepted: 27 September 2024  \nKeywords: applied education | information systems | machine learning | random Forest | Tourism and Hospitality  \nABSTRACT  \nIn response to the COVID-19 pandemic, an abrupt wave of digitisation and online migration swept the higher education institutions around the globe. In the aftermath of this digital transformation which endures as the legacy of the pandemic, what lacksin knowledge is how effective the anti-COVID measures were in maintaining quality education. Using machine learning to analyse student grades as a proxy for educational standards, this study investigates and demonstrates the evaluative potential of machine learning (vs. traditional statistics) with respect to not only crisis responses in education but also applied studies such as Information Systems and Tourism. Main implication of this study is the analytical utility of machine learning even when educational data are irregular and small. However, incorporating accurate and meaningful data points into the existing online educational systems is crucial to leverage this utility of machine learning.  \n1 | Introduction  \nUNESCO considers educational attainment as one of the main indicators of social sustainability (UNESCO 2017; UN 2022) . In this sense, COVID-19 pandemic caused a major setback in sustainable development as the global average of higher education graduation rates, after two decades of steady increase, fell by 5% in 2020. Higher education institutions around the world shared the common concern for safeguarding the quality of their education from the disruptive effects of the pandemic (UNESCO 2024) . Consequently, most universities around the globe hastily adjusted their instructive methods and transitioned to online learning systems. This transition mainly consisted of fully online classes on digital learning management platforms (Crawford et al. 2020; Montenegro-Rueda et al. 2021), videoconference tools (e.g., Zoom) or proctoring programs (e.g., Respondus) .  \nStudies have been emerging that describe the instructional transformation and the evaluative changes universities have gone through during the pandemic (Crawford et al. 2020; Bao 2020; Toquero 2020; Yan 2020); however, the extent or the impact of these adjustments remains understudied (Karalis 2020) .  \nAlthough the COVID-19 may have largely subsided, the legacy of the pandemic is substantial in terms of the digitisation of education systems permeating instructional platforms, assessment tools as well as administrative functions (Geisinger 2022; Markle 2023). Regardless of which types of learning platforms or assistive tools were chosen as anti-COVID measures, the rapid online transition as well as other restrictions imposed on education due to social distancing mandates indisputably created a challenging experience for most, if not all, university communities (Taylor et al. 2022) . Hence, a salient gap in current studies is the effectual evaluation of anti-COVID  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \n© 2024 The Author(s). European Journal of Education published by John Wiley & Sons Ltd. 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