ReviewJournal of education and health promotion2025
Educational data mining in medical education: A five-level approach.
Review in Journal of education and health promotion, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The healthcare industry in each country has one of the most important and sophisticated educational systems that produces and stores a large amount of educational data daily. Data generated by the interaction of managers, patients, instructors, students, employees, and all those who are involved with educational systems can revolutionize medical education through analysis and prediction of the hidden patterns of knowledge, skills, and attitude that have been neglected in this massive amount of data. This study aims to review data mining in medical education and provide a comprehensive picture of it in different educational dimensions. In this study, we performed a literature review from 2010 to 2022 in IEEE, SSCI, Elsevier, CIVILICA, and Science Direct. Two hundred and fifty articles were identified. In total, 34 documents were included in the study. Interned articles' methodological quality was assessed using the five-step method proposed by Carnwell and Daly. This method is used for summarizing texts, summarizing points of view, and finally providing a line of guidance for future research. A five-level taxonomy was developed in this study which includes educational policy and management, instructional designing and planning, educational technologies, learning content, and learning outcomes. To increase the efficiency of data mining techniques at each level, some useful recommendations were presented in more detail. Educational data mining (EDM) as a new methodology can lead to better policy-making, more proper planning, and more effective decisions. EDM by extracting data makes it easier to describe and predict educational trends, which can guarantee the success of medical education more than before.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.