Data mining in education
Abstract
Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Business and Industry
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Prior studies have demonstrated the effectiveness of machine learning techniques-such as clustering, classification, and sequence mining-in modeling learner behavior and predicting academic performance.
“Prior studies have demonstrated the effectiveness of machine learning techniquessuch as clustering, classification, and sequence mining-in modeling learner behavior and predicting academic performance [12].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [12]
Cites
Educational Data Mining: A review of the state of the art
Romero, C., & Ventura, S. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2053532 · Cited by 2,251 · see citing works
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Application and theory gaps during the rise of artificial intelligence in education
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003 · see citing works
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2019.01.007 · Cited by 564 · see citing works
Visual learning analytics of educational data: A systematic literature review and research agenda
Vieira, C., Parsons, P., & Byrd, V. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.03.018 · Cited by 316 · see citing works
The efficacy of learning analytics interventions in higher education: a systematic review.
Larrabee Sønderlund, A., Hughes, E., & Smith, J. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12720 · Cited by 265 · see citing works
A Systematic Review of Deep Learning Approaches to Educational Data Mining
Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019) · Complexity · doi:10.1155/2019/1306039 · Cited by 230 · see citing works
What to teach? Strategies for developing digital competency in preservice teacher training
Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104149 · Cited by 215 · see citing works
Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning
Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107547 · Cited by 198 · see citing works
An early feedback prediction system for learners at-risk within a first-year higher education course
Baneres, D., Rodriguez, M. E., & Serra, M. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2912167 · Cited by 128 · see citing works
Learning analytics in higher education: a preponderance of analytics but very little learning?
Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., & Lorca-Vyhmeister, A. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00258-x · Cited by 131 · see citing works
Okoye, K., Arrona-Palacios, A., Camacho-Zuñiga, C., Achem, J. A. G., Escamilla, J., & Hosseini, S. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10751-5 · Cited by 112 · see citing works
A review of machine learning methods used for educational data
Ersozlu, Z., Taheri, S., & Koch, I. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12704-0 · Cited by 96 · see citing works
“With the identification of social connec - tions and patterns among students, this method enables educators to pinpoint student groups that may require further assistance and targeted interventions (Romero & Ventura, 2013).”
Charitopoulos, A., Rangoussi, M., & Koulouriotis, D. (2020) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00200-8 · Cited by 89 · see citing works
Educator perspectives on learning analytics in classroom practice
Shibani, A., Knight, S., & Buckingham Shum, S. (2020) · The Internet and Higher Education · doi:10.1016/j.iheduc.2020.100730 · Cited by 86 · see citing works
Foster, C., & Francis, P. (2020) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1696945 · Cited by 55 · see citing works
A Review of Emotion-Aware Systems for e-Learning in Virtual Environments
Feidakis, M. (2016) · Formative Assessment, Learning Data Analytics and Gamification · doi:10.1016/b978-0-12-803637-2.00011-7 · Cited by 54 · see citing works
Recent trends in computational intelligence for educational big data analysis
Ikegwu, A. C., Nweke, H. F., & Anikwe, C. V. (2024) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00158-5 · Cited by 53 · see citing works
Quadir, B., Chen, N.-S., & Isaias, P. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2020.1712427 · Cited by 46 · see citing works
AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs
Angeioplastis, A., Konstantakis, M., Aliprantis, J., Ordoumpozanis, K., Varsamis, D., & Tsimpiris, A. (2026) · Information · doi:10.3390/info17020216 · Cited by 3 · see citing works
“Building on prior work in educational data mining and learning analytics, PREPARE continuously analyzes learner interactions—such as navigation patterns, resource consumption, assessment attempts, and conversational exchanges—to update learner profiles over time [2,12].”
- Authors
- Romero, C., & Ventura, S.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2012-12-14
- Volume
- 3
- Issue
- 1
- Pages
- 12-27
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W2138334515
- Cited by
- 869
- References
- 1
- Citations by year
- 2026: 32 · 2025: 61 · 2024: 62 · 2023: 93 · 2022: 76 · 2021: 77 · 2020: 81 · 2019: 86 · 2018: 86 · 2017: 61 · 2016: 60 · 2015: 56 · 2014: 24 · 2013: 14
- FWCI
- 48.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- educational data mining, student learning analysis, educational information systems, learning analytics, educational outcomes
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