The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review by Carlos Merino-Campos

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Predicting student retention: A comparative study of machine learning approach utilizing sociodemographic and academic factors by Reymark D. Deleña, Norniña J. Dia, Redeemtor R. Sacayan, Joseph C. Sieras, Suhaina A. Khalid, Amer Hussien T. Macatotong, Sacaria B. Gulam

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Analyzing click data with AI: implications for student performance prediction and learning assessment by Mahdi-Reza Borna, Hanan Saadat, Aref Tavassoli Hojjati, Elham Akbari

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Can electronic monitoring with a digital smart spacer support personalised medication adherence and inhaler technique education in patients with asthma?: Protocol of the randomised... by Job F. M. van Boven, Boudewijn H.J. Dierick, Maria Achterbosch, Sandra Been-Buck, Titia Klemmeier, Susanne J. van de Hei, Paul Hagedoorn, Huib A.M. Kerstjens, Janwillem W.H. Kocks

“…The aim of this study is to assess the feasibility of undertaking a definitive randomised controlled trial of personalised, smart spacer data-driven education and explore clinical benefits.Methods and analysis We present the design of the multicentre, randomised controlled OUtcomes following Tailored Education and Retraining: Studying Performance and AdherenCE feasibility trial of 2 months. …”
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Master Data Management in Higher Education: Enhancing Effectiveness Study on Universities from Romania and Germany by Matthias SCHMUCK, Mircea GEORGESCU

“…These insights aim to help university administrators create efficient, data-driven educational ecosystems supported by modern IT infrastructure, ultimately aligning with academic and operational objectives.…”
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A conceptual approach to digital transformation of the educational process at a higher education institution by A. A. Kytmanov, Yu. N. Gorelova, T. V. Zykova, O. A. Pikhtilkova, E. V. Pronina

“…The development of a conceptual approach for creating a data-driven educational process management system at a university is becoming a priority task, whose successful execution will underpin further university advancement and competitiveness.…”
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Advancing statistical literacy for prospective teachers: A systematic review of contemporary approaches and best practices by Rahma Siska Utari, Ratu Ilma Indra Putri, Zulkardi, Hapizah

“…This study employs a Systematic Literature Review (SLR) to explore the development of statistical literacy among prospective teachers, which is crucial in today’s data-driven educational landscape. The review synthesizes findings from 18 selected studies published between 2015 and 2024, focusing on various pedagogical approaches, challenges in implementation, and strategies for enhancing statistical literacy instruction. …”
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Analysis of Meeting Early Childhood Education Resource Needs Through Monte Carlo Simulation by Citra Aulia Uzliva, Philips Ratu Bunga, Yenik Wahyuningsih

“…This research contributes to developing data-driven educational planning practices, particularly in resource-limited settings. …”
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A Proposed Instrument for Mapping Teacher Digital Competence: Design and Conceptual Framework by Tepati Hak Kewajiban

“…Although the instrument is currently at the conceptual stage, it holds significant potential for application in TDC mapping studies, evaluations of professional development programs, and the formulation of data-driven educational policies. Further empirical validation is recommended prior to broader implementation …”
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A comparative analysis of feedback delivery modality within a Precision Teaching protocol to enhance drivers’ lane maintenance by Mariaelena Tagliabue, Giulia De Cet, Federico Orsini, Massimiliano Gastaldi, Riccardo Rossi

“…Precision Teaching (PT) is a data-driven educational technique that continuously monitors and adjusts instruction to achieve specific, measurable learning outcomes. …”
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Interpretable Machine Learning Models for PISA Results in Mathematics by Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez

“…To address this question, we proposed using advanced Machine Learning techniques through possibly non-linear predictive models that identify key drivers of Mathematics performance to inform data-driven educational policies and interventions that improve learning outcomes. …”
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A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

“…The findings could inform strategies to enhance principals' job satisfaction and strengthen data-driven educational policies.…”
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