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Educational Data Mining: A Review of the State of the Art
Abstract
Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.
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“This personalized approach helps optimize learning outcomes by focusing on areas where learners need more support and practice [87].”
From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [87]
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Huang, T.-C., Cheng, S.-C., & Huang, Y. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.01.039 · Cited by 40
The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks
Pardos, Z. A., Heffernan, N. T., Anderson, B. S., & Heffernan, C. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73078-1_60 · Cited by 40
PeRES: A Personalized Recommendation Education System Based on Multi-agents & SCORM
Zhu, F., Ip, H. H. S., Fok, A. W. P., & Cao, J. (2008) · Springer eBooks · doi:10.1007/978-3-540-78139-4_4 · Cited by 40
Automatic Detection of Student Mental Models during Prior Knowledge Activation in MetaTutor.
Rus, V., Lintean, M. C., & Azevedo, R. (2009) · Educational Data Mining · Cited by 39
Distance education: a Web usage mining case study for the evaluation of learning sites
Machado, L. D. S., & Becker, K. E. (2004) · doi:10.1109/icalt.2003.1215123 · Cited by 39
Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques
Ogor, E. N. (2007) · Cited by 39
Mining and visualizing visited trails in web-based educational systems
Romero, C., Gutiérrez-Santos, S., Freire, M., & Ventura, S. (2008) · BIROn (Birkbeck, University of London) · Cited by 38
Self-citationSupporting teachers in collaborative student modeling: A framework and an implementation
Gaudioso, E., Montero, M. A., Talavera, L., & Hernández‐del‐Olmo, F. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.12.035 · Cited by 37
Data mining and text mining technologies for collaborative learning in an ILMS "Ssamurai"
Ueno, M. (2004) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2004.1357749 · Cited by 37
Using Association Rules to Guide a Search for Best Fitting Transfer Models of Student Learning
Freyberger, J. E. (2004) · Cited by 36
Data Mining and Case-Based Reasoning for Distance Learning
Shen, R., Han, P., Yang, F., Yang, Q., & Huang, J. Z. (2003) · International Journal of Distance Education Technologies · doi:10.4018/jdet.2003070104 · Cited by 36
A Rule-Based Recommender System for Online Discussion Forums
Abel, F., Bittencourt, I. I., Henze, N., Krause, D., & Vassileva, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70987-9_4 · Cited by 36
The relationship of learning traits, motivation and performance-learning response dynamics
Hwang, W., Chang, C.-B., & Chen, G.-J. (2003) · Computers & Education · doi:10.1016/j.compedu.2003.08.004 · Cited by 36
Acquiring Background Knowledge for Intelligent Tutoring Systems.
Antunes, C. (2008) · Cited by 35
Wang, F.-H. (2008) · Cited by 34
Pixed: an ITS that guides students with the help of learners' interaction log
Héraud, J.-M., France, L., & Mille, A. (2004) · Cited by 34
Ben-Naim, D., Bain, M., & Marcus, N. (2009) · Educational Data Mining · Cited by 33
'First Aid for You': getting to know your learning style using machine learning
Kelly, D., & Tangney, B. (2005) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2005.1 · Cited by 33
SAMOS: a model for monitoring students' and groups' activities in collaborative e-learning
Juan, A. A., Daradoumis, Τ., Faulín, J., & Xhafa, F. (2009) · International Journal of Learning Technology · doi:10.1504/ijlt.2009.024716 · Cited by 32
The use of data mining in education environment
Vranić, M., Pintar, D., & Skočir, Z. (2007) · doi:10.1109/contel.2007.381878 · Cited by 32
Using data mining for improving web-based course design
Myller, N., Suhonen, J. T., & Sutinen, E. (2003) · doi:10.1109/cie.2002.1186125 · Cited by 32
Web Usage Mining to Evaluate the Transfer of Learning in a Web-Based Learning Environment
Chanchary, F., Haque, I., & Khalid, M. S. (2008) · doi:10.1109/wkdd.2008.139 · Cited by 32
Pardos, Z. A., Heffernan, N. T., Ruiz, C., & Beck, J. E. (2008) · Educational Data Mining · Cited by 31
Looking for Sources of Error in Predicting Student's Knowledge
Feng, M., Heffernan, N. T., & Koedinger, K. R. (2005) · Cited by 31
Temporal Data Mining for Educational Applications
Beal, C. R., & Cohen, P. R. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89197-0_10 · Cited by 31
Determining the Significance of Item Order In Randomized Problem Sets
Pardos, Z. A., & Heffernan, N. T. (2009) · Educational Data Mining · Cited by 30
Predicting Student Outcomes Using Discriminant Function Analysis.
Martinez, D. H. (2001) · Cited by 29
Alfonseca, E., Rodrı́guez, P., & Pérez-Marín, D. (2005) · Computers & Education · doi:10.1016/j.compedu.2005.10.007 · Cited by 29
Lee, C. (2005) · Computers & Education · doi:10.1016/j.compedu.2005.10.010 · Cited by 29
Learning what is important: feature selection and rule extraction in a virtual course
Etchells, T. A., Àngela Nebot, B., Vellido, A., Lisboa, P. J. G., & Mugica, F. (2006) · Cited by 28
Does Self-Discipline impact students' knowledge and learning?
Gong, Y., Rai, D., Beck, J. E., & Heffernan, N. T. (2009) · Cited by 28
LOCO-Analyst: A Tool for Raising Teachers’ Awareness in Online Learning Environments
Jovanović, J., Gašević, D., Brooks, C. A., Devedžić, V., & Hatala, M. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-75195-3_9 · Cited by 28
Predicting Students’ Performance with SimStudent: Learning Cognitive Skills from Observation
Matsuda, N., Cohen, W. W., Sewall, J., Lacerda, G., & Koedinger, K. R. (2018) · Figshare · doi:10.1184/r1/6470330 · Cited by 28
Data mining technology for the evaluation of web-based teaching and learning systems
Pahl, C., & Donnellan, D. (2002) · Arrow@dit (Dublin Institute of Technology) · Cited by 28
Cluster Analysis for Users’ Modeling in Intelligent E-Learning Systems
Zakrzewska, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69052-8_22 · Cited by 27
Research on Personalized E-Learning System Using Fuzzy Set Based Clustering Algorithm
Lu, F., Li, X., Liu, Q., Yang, Z., Tan, G., & He, T. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72588-6_99 · Cited by 27
Research on E-learner Personality Grouping Based on Fuzzy Clustering Analysis
Tian, F., Wang, S., Zheng, C. E., & Zheng, Q. (2008) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2008.4537122 · Cited by 27
Detecting Symptoms of Low Performance Using Production Rules.
Bravo, J., & Ortigosa, Á. (2009) · Cited by 26
Learning Activity-Based E-Learning Material Recommendation System
Liu, F., & Shih, B. (2007) · International Symposium on Multimedia · doi:10.1109/ismw.2007.53 · Cited by 26
Data Analysis Center Based on E-Learning Platform
Shen, R., Yang, F., & Han, P. (2002) · Springer eBooks · doi:10.1007/978-94-010-0494-7_3 · Cited by 25
Ranjan, J., & Khalil, S. (2007) · Information Technology Journal · doi:10.3923/itj.2008.16.23 · Cited by 25
Feng, M., Heffernan, N. T., Heffernan, C., & Mani, M. (2009) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2009.17 · Cited by 25
Identification of fuzzy models to predict students performance in an e-learning environment
Àngela Nebot, B., Castro, F., Mugica, F., & Vellido, A. (2006) · International Conference on Web-based Education · Cited by 23
Analysing users' access logs in Moodle to improve e learning
Baruque, C. B., Amaral, M. A., Barcellos, A., Freitas, J. C. D. S., & Longo, C. R. J. (2007) · Euro American Conference on Telematics and Information Systems · doi:10.1145/1352694.1352767 · Cited by 23
Evaluation of learning in computer based education using log systems
Rahkila, M., & Karjalainen, M. (2003) · doi:10.1109/fie.1999.839266 · Cited by 23
Planning of educational training courses by data mining: Using China Motor Corporation as an example
Huang, C.-T., Lin, W., Wang, S.-T., & Wang, W.-S. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.009 · Cited by 23
Towards Fuzzy Domain Ontology Based Concept Map Generation for E-Learning
Lau, R. Y. K., Chung, A. Y. K., Song, D., & Huang, Q. (2008) · Springer eBooks · doi:10.1007/978-3-540-78139-4_9 · Cited by 23
Discovering enrollment knowledge in university databases
Sanjeev, A. P., & Żytkow, J. M. (1995) · Knowledge Discovery and Data Mining · Cited by 22
Machine Learning Approaches for Inducing Student Models
Licchelli, O., Basile, T. M. A., Mauro, N. D., Esposito, F., Semeraro, G. M., & Ferilli, S. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24677-0_96 · Cited by 22
Using Dirichlet Priors to Improve Model Parameter Plausibility.
Rai, D., Gong, Y., & Beck, J. E. (2009) · Educational Data Mining · Cited by 22
Data Mining in Higher Education
Llorente, R., & Morant, M. (2011) · InTech eBooks · doi:10.5772/13320 · Cited by 22
García, R. M. C., Pardo, A., Pérez, J. P. S., & Kloos, C. D. (2005) · Lecture notes in computer science · doi:10.1007/11504894_95 · Cited by 22
Personalized Instructing Recommendation System Based on Web Mining
Zhang, L., Liu, X., & Liu, X. (2008) · doi:10.1109/icycs.2008.115 · Cited by 22
A Computerized Approach to Diagnosing Student Learning Problems in Health Education
Chu, H., Hwang, G., Tseng, J. C. R., & Hwang, G. (2006) · doi:10.6412/ajhis.200604.0043 · Cited by 22
Evaluating the Feasibility of Learning Student Models from Data
Jönsson, A., Johns, J., Mehranian, H., Arroyo, I., Woolf, B. P., Barto, A. G., Fisher, D., & Mahadevan, S. (2005) · Cited by 21
eLORM: learning object relationship mining‐based repository
Ouyang, Y., & Zhu, M. (2008) · Online Information Review · doi:10.1108/14684520810879863 · Cited by 21
0 Rhythm and Randomness in Human Contact
Freeman, M. P., Watkins, N. W., Yoneki, E., Crowcroft, J., et al. (2010) · Cited by 21
Analyzing Rule Evaluation Measures with Educational Datasets: A Framework to Help the Teacher
Ventura, S., Romero, C., & Hervás‐Martínez, C. (2008) · Cited by 20
Self-citationData Mining for Evaluation, Benchmarking and Reflective Practice in a LMS
Heathcote, E., & Dawson, S. (2005) · QUT ePrints (Queensland University of Technology) · Cited by 20
Student progress monitoring tool using treeview
Yoo, J. P., Yoo, S., Lance, C., & Hankins, J. (2006) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/1121341.1121456 · Cited by 20
Profiling Students Who Take Online Courses Using Data Mining Methods.
Yu, C. H., DiGangi, S. A., Jannasch-Pennell, A. K., & Kaprolet, C. M. (2008) · Online journal of distance learning administration · Cited by 19
Minel: a framework for mining e-learning logs
Bellaachia, A., Vommina, E., & Berrada, B. (2006) · International Conference on Web-based Education · Cited by 19
The application of SOM as a decision support tool to identify AACSB peer schools
Kiang, M. Y., Fisher, D. M., Chen, V., Fisher, S. A., & Chi, R. T. (2009) · Decision Support Systems · doi:10.1016/j.dss.2008.12.010 · Cited by 19
Evaluating learning behavior of Web-Based Training (WBT) using Web log
Wu, A. K.-W., & Leung, C.-H. (2003) · doi:10.1109/cie.2002.1186062 · Cited by 19
Conditional subspace clustering of skill mastery: identifying skills that separate students
Nugent, R., Ayers, E., & Dean, N. (2009) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · Cited by 18
Feng, M., Beck, J. E., & Heffernan, N. T. (2009) · Educational Data Mining · Cited by 18
Bayesian Student Models Based on Item to Item Knowledge Structures
Desmarais, M. C., & Gagnon, M. (2006) · Lecture notes in computer science · doi:10.1007/11876663_11 · Cited by 18
An Improvement of Matrix-based Clustering Method for Grouping Learners in E-Learning
Zhang, K., Cui, L., Wang, H., & Sui, Q. (2007) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2007.4281577 · Cited by 18
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Metadata
- Authors
- Romero, C., & Ventura, S.
- Affiliations
- University of Córdoba (ES)
- Venue
- IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
- Published
- 2010-11
- Volume
- 40
- Issue
- 6
- Pages
- 601-618
- ISSN
- 1094-6977 (ISSN-L)
- 1558-2442
- OpenAlex
- W2006444123
- Cited by
- 2,251
- References
- 279
- Citations by year
- 2026: 204 · 2025: 141 · 2024: 117 · 2023: 118 · 2022: 142 · 2021: 144 · 2020: 204 · 2019: 196 · 2018: 185 · 2017: 167 · 2016: 155 · 2015: 145 · 2014: 140 · 2013: 90 · 2012: 70
- FWCI
- 147.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (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
- Social Sciences › Education › Educational Technology and Pedagogy
- Keywords
- educational data mining, educational data analysis, learning analytics, educational environment, future research directions
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