Educational data mining: a 10-year review
| dc.contributor.author | Kalita, Emi | |
| dc.contributor.author | Oyelere, Solomon Sunday | |
| dc.contributor.author | Gaftandzhieva, Silvia | |
| dc.contributor.author | Rajesh, Kandala N. V. P. S. | |
| dc.contributor.author | Jagatheesaperumal, Senthil Kumar | |
| dc.contributor.author | Mohamed, Asmaa | |
| dc.contributor.author | Elbarawy, Yomna M. | |
| dc.date.accessioned | 2025-07-03T21:26:52Z | |
| dc.date.issued | 2025 | |
| dc.department | Balıkesir Üniversitesi | |
| dc.description.abstract | This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study's contributions and limitations. | |
| dc.description.sponsorship | Lulea University of Technology | |
| dc.description.sponsorship | The authors are thankful to all the individuals who helped directly or indirectly to complete this study. For open access, the author has applied a 'Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version. | |
| dc.identifier.doi | 10.1007/s10791-025-09589-z | |
| dc.identifier.issn | 2948-2984 | |
| dc.identifier.issn | 2948-2992 | |
| dc.identifier.issue | 1 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://doi.org/10.1007/s10791-025-09589-z | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12462/21909 | |
| dc.identifier.volume | 28 | |
| dc.identifier.wos | WOS:001490873500003 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.language.iso | en | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Discover Computing | |
| dc.relation.publicationcategory | Diğer | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_WOS_20250703 | |
| dc.subject | Education data mining | |
| dc.subject | Multimodal learning analytics | |
| dc.subject | Artificial intelligence in education | |
| dc.subject | Explainability in education | |
| dc.title | Educational data mining: a 10-year review | |
| dc.type | Review Article |












