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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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Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
Draper, N. R., & Smith, H. (1998) · Wiley series in probability and statistics · doi:10.1002/9781118625590 · Cited by 5,086
Gunst, R. F., Draper, N. R., & Smith, H. C. (1999) · Technometrics · doi:10.2307/1270576 · Cited by 4,071
Cluster analysis for researchers
(1987) · Environmental Software · doi:10.1016/0266-9838(87)90010-4 · Cited by 1,850
A teoria subjacente aos mapas conceituais e como elabora-los e usa-los
Novak, J. D., & Cañas, A. J. (2010) · Praxis Educativa · doi:10.5212/praxeduc.v.5i1.009029 · Cited by 1,681
Educational data mining: A survey from 1995 to 2005
Romero, C., & Ventura, S. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.04.005 · Cited by 1,490
Self-citationThe State of Educational Data Mining in 2009: A Review and Future Visions
Baker, R. S. J. D., & Yacef, K. (2009) · Journal of Educational Data Mining · doi:10.5281/zenodo.3554657 · Cited by 1,365
The State of Educational Data Mining in 2009: A Review and Future Visions
Baker, R. S., & Yacef, K. (2009) · Journal of Educational Data Mining · doi:10.5281/zenodo.3554658 · Cited by 1,296
Data mining in course management systems: Moodle case study and tutorial
Romero, C., Ventura, S., & Salcines, E. G. (2007) · Computers & Education · doi:10.1016/j.compedu.2007.05.016 · Cited by 1,094
Self-citationCluster Analysis for Researchers
Milligan, G. W., & Romesburg, H. C. (1985) · Journal of Marketing Research · doi:10.2307/3151374 · Cited by 1,009
Flagged first, then by citations
8 of the 279 references couldn’t be matched to a known paper.
Research works printed but not linked: 53
- mazza, 2003, the design of a course data visualizator: an empirical study, Proc Int Conf New Educ Environ, 215
- mcdonald, 2004, predicting student success, J Math Teaching Learning, 1, 1
- ben-naim, 2008, visualization and analysis of student interaction in an adaptive exploratory learning environment, Proc Int Workshop Intell Support Exploratory Environ Eur Conf Technol Enhanced Learn, 1
- beck, 2005, 20th Nat Conf Artif Intell (AAAI) Workshop Educ Data Mining
- beck, 2004, 7th Int Conf Workshop Analyzing Student-tutor Interaction Logs Improve Educ Outcomes
- beck, 2000, 5th Int Conf Intell Tutoring Syst (ITS) Workshop Applying Mach Learning to ITS Design/Construction
- beck, 2007, 7th IEEE Int Conf Adv Learn Technol Workshop Educ Data Mining
- beck, 2006, 21st Nat Conf Artif Intell (AAAI) Workshop Educ Data Mining
- yudelson, 2006, mining student learning data to develop high level pedagogic strategy in a medical its, Proceedings of AAAI2005 workshop on Educational Data Mining, 1
- yang, 2002, an agent-based recommender system for lesson plan sequencing, Proc Int Conf Adv Learning Technol, 14
- winters, 2005, topic extraction from item-level grades, Proceedings of AAAI2005 workshop on Educational Data Mining, 7
- mostow, 2005, An educational data mining tool to browse tutor-student interactions: Time will tell!, Proc Workshop Educational Data Mining, 15
- muehlenbrok, 2005, automatic action analysis in an interactive learning environment, Proc 12th Int Conf Artif Intell Educ Workshop Usage Anal Learn Syst, 73
- barnes, 2009, 2nd Int Conf Educ Data Mining
- baker, 2010, 3rd Int Conf Educ Data Mining
- chen, 2007, mining key formative assessment rules based on learner portfiles for web-based learning systems, Proc IEEE Int Conf Adv Learn Technol, 1
- zafra, 2009, predicting student grades in learning management systems with multiple instance programming, Proc Int Conf Educ Data Mining, 307
- kristofic, 2005, recommender system for adaptive hypermedia applications, Proc Stud Res Conf Informat Inf Technol, 229
- yu, 2001, on learning behavior analysis of web based interactive environment, Proc Int Conf Elect Electron Comp Eng, 1
- burr, 2004, pattern of user behavior in university online forums, Int'l J Instructional Technology and Distance Learning, 1, 11
- markellou, 2005, using semantic web mining technologies for personalized e-learning experiences, Web-based education, 461
- castro, 2007, Evolution of Teaching and Learning Paradigms in Intelligent Environment (Studies in Computational Intelligence) vol 62, 183
- choquet, 2005, Artif Intell Educ Conf (AIED) Workshop Usage Analysis Learning Syst
- zukhri, 2007, solving new student allocation problem with genetic algorithms: a hard problem for partition based approach, J Zhejiang Univ, 2, 6
- delgado, 2006, Predicting students' marks from. Moodle logs using neural network models, Proc Int Conf Current Dev Technol -Assist Educ, 586
- gibbs, 2003, evaluating student behavior on instructional web sites using web server logs, Proc Ninth Sloan-C Int Conf Online Learn, 1
- nesbit, 2008, sequential pattern analysis software for educational event data, Proc Int Conf Methods Tech Behav Res
- girones, 2006, ariadne, a guiding thread in the learning process's labyrinth, Proc Int Conf Current Dev Technol -Assist Educ, 287
- feng, 2006, informing teachers live about student learning: reporting in the assistment system, Technol Instruction Cognition Learn J, 3, 1
- freedman, 2007, Statistics
- ramli, 2005, web usage mining using a priori algorithm: uum learning care portal case, Proc Int Conf Inf Knowl Manage, 1
- rallo, 2005, using data mining and social networks to analyze the structure and content of educative online communities, Proc Int Conf Multimedia ICTs Educ, 1
- romero, 2007, Proc Int Workshop Applying Data Mining E-Learning (ADML) 2nd Eur Conf Technol Enhanced Learn (EC-TEL07)
- ibrahim, 2007, predicting students' academic performance: comparing artificial neural network, decision tree and linear regression, Proc Annu SAS Malaysia Forum, 1
- rosta, 2006, social navigation support in a course recommendation system, Proc Int Conf Adaptive Hypermedia Adaptive Web-Based Syst, 1
- kay, 2006, mining patterns of events in students' teamwork data, Proc Workshop Educational Data Mining, 1
- kim, 2006, mining and assessing discussions on the web through speech act analysis, Proc AAAI Workshop Web Content Mining Human Lang Technol, 1
- kotsiantis, 2003, preventing student dropout in distance learning systems using machine learning techniques, Proc 3rd Int Conf Knowl -Based Intell Inf Eng Syst, 3
- spacco, 2006, inferring use cases from unit testing, Proc Workshop Educational Data Mining, 1
- simko, 2009, automatic concept telationships discovery for an adaptive e-course, Proc Int Conf Educ Data Mining, 171
- heiner, 2006, 8th Int Conf Intell Tutoring Syst (ITS) Workshop Educ Data Mining
- heiner, 2007, 13th Int Conf Artif Intell Educ Workshop Educ Data Mining
- talavera, 2004, mining student data to characterize similar behavior groups in unstructured collaboration spaces, Proc Workshop Artif Intell CSCL, 17
- tang, 2005, smart recommendation for an evolving e-learning system, Int J E-Learning, 4, 105
- avouris, 2005, why logging of fingertip actions is not enough for analysis of learning activities, Proc AIED Conf Workshop Usage Anal Learn Syst, 1
- ayers, 2006, Do skills combine additively to predict task difficulty in eighth grade mathematics?, Proceedings of AAAI2005 workshop on Educational Data Mining, 14
- baker, 2007, track on educational data mining, 11th Int Conf User Model Workshop Data Mining User Model
- hien, 2007, a decision support system for evaluating international student applications, Proc Frontiers Educ Conf, 1
- baker, 2008, 1st Int Conf Educ Data Mining
- hbscher, 2007, Domain specific interactive data mining, Proc 11th Int Conf User Model Workshop Data Mining User Model, 81
- ueno, 2002, Learning log database and data mining system for e-learning—on line statistical outlier detection of irregular learning processes, Proc Int Conf Adv Learning Technol, 436
- vee, 2006, understanding novice errors and error paths in object-oriented programming through log analysis, Proc Workshop Educational Data Mining, 13
- vellido, 2007, Evolution of Teaching and Learning Paradigms in Intelligent Environment Studies in Computational Intelligence (SCI) (Advanced Information and Knowledge Processing) vol 62, 223
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E-learning and educational data mining in cloud computing: an overview
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Jacob, J. S., Jha, K., Kotak, P., & Puthran, S. (2015) · International Conference on Green Computing and Internet of Things (ICGCIoT) · doi:10.1109/icgciot.2015.7380675 · Cited by 47
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Daniel, B. K., & Butson, R. J. (2013) · International Association for Development of the Information Society · Cited by 47
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Mimis, M., Hajji, M. E., Es-Saady, Y., Guejdi, A. O., Douzi, H., & Mammass, D. (2018) · Education and Information Technologies · doi:10.1007/s10639-018-9838-8 · Cited by 47
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Alshareef, F., Alhakami, H., Alsubait, T., & Baz, A. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110494 · Cited by 46
Şahi̇n, M., & Yurdugül, H. (2020) · Technology Knowledge and Learning · doi:10.1007/s10758-020-09479-x · Cited by 46
Anaya, A. R., & Boticario, J. G. (2009) · Educational Data Mining · Cited by 45
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Bixler, R., & D'Mello, S. K. (2015) · User Modeling, Adaptation, and Personalization · doi:10.1007/978-3-319-20267-9_3 · Cited by 46
Troussas, C., Krouska, A., & Virvou, M. K. (2019) · Intelligent systems reference library · doi:10.1007/978-3-030-13743-4_2 · Cited by 45
On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining
Ramaswami, G., Sušnjak, T., & Mathrani, A. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6010006 · Cited by 35
Su, Y.-S., & Wu, S.-Y. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02712-6 · Cited by 44
RetractedPrakash, J., Swathiramya, R., Balambigai, G., Menaha, R., & Abhirami, J. S. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.780 · Cited by 44
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Garcia, E. P. I., & Mora, P. M. (2011) · Mexican International Conference on Artificial Intelligence · doi:10.1109/micai.2011.28 · Cited by 44
Yera, R., & Mota, Y. C. (2014) · International Journal of Distance Education Technologies · doi:10.4018/ijdet.2014040103 · Cited by 44
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Kumar, V., & Chadha, A. (2012) · Cited by 44
Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets
Navarro, Á. M., & Pablo Moreno-ger, A. (2018) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2018.02.003 · Cited by 44
New methods and technologies for enhancing usability and accessibility of educational data
Fonseca, D., García‐Peñalvo, F. J., & Camba, J. D. (2020) · Universal Access in the Information Society · doi:10.1007/s10209-020-00765-0 · Cited by 54
Review on publicly available datasets for educational data mining
Mihăescu, M. C., & Popescu, P. Ș. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1403 · Cited by 44
Hershkovitz, A., Baker, R. S., Gobert, J. D., Wixon, M., & Pedro, M. S. (2013) · American Behavioral Scientist · doi:10.1177/0002764213479365 · Cited by 49
Multi-view hypergraph neural networks for student academic performance prediction
Li, M., Zhang, Y., Li, X., Cai, L., & Yin, B. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105174 · Cited by 44
Educational data mining: a 10-year review
Kalita, E., Oyelere, S. S., Gaftandzhieva, S. N., Rajesh, K. N. V. P. S., Jagatheesaperumal, S. K., Mohamed, A. S. A., Elbarawy, Y. M., Desuky, A. S., Hussain, S., Çifçi, M. A., et al. (2025) · Discover Computing · doi:10.1007/s10791-025-09589-z · Cited by 66
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Walldén, S., & Mäkinen, E. (2014) · Informatics in Education · doi:10.15388/infedu.2014.08 · Cited by 43
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Popescu, E., & Leon, F. (2018) · IEEE Access · doi:10.1109/access.2018.2882297 · Cited by 43
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García‐Peñalvo, F. J. (2020) · Lecture notes in educational technology · doi:10.1007/978-981-15-4526-9_1 · Cited by 43
Kovanović, V., Joksimovíc, S., Gašević, D., & Hatala, M. (2014) · Cited by 42
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Chaudhury, P., Mishra, S., Tripathy, H. K., & Mishra, B. K. (2016) · International Conference on Information and Communication Technology for Competitive Strategies (ICTCS) · doi:10.1145/2905055.2905150 · Cited by 42
Creating student’s profile using blockchain technology
Juričić, V., Radošević, M., & Fuzul, E. (2019) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro.2019.8756687 · Cited by 42
Tissenbaum, M. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103739 · Cited by 42
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Halverson, R., & Owen, V. E. (2014) · International Journal of Learning Technology · doi:10.1504/ijlt.2014.064489 · Cited by 41
Sedrakyan, G., Snoeck, M., & Weerdt, J. D. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.09.054 · Cited by 41
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Khribi, M. K., Jemni, M., & Nasraoui, O. (2014) · Lecture notes in educational technology · doi:10.1007/978-3-662-44659-1_9 · Cited by 40
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Λαμπρόπουλος, Γ., & Evangelidis, G. (2025) · Applied Sciences · doi:10.3390/app15020971 · Cited by 65
Cho, M., & Yoo, J. S. (2016) · Interactive Learning Environments · doi:10.1080/10494820.2016.1232278 · Cited by 42
Khanna, L., Singh, S. N., & Alam, M. (2016) · India International Conference on Information Processing (IICIP) · doi:10.1109/iicip.2016.7975354 · Cited by 39
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Wang, Q., & Mousavi, A. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13282 · Cited by 38
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Grivokostopoulou, F., Perikos, I., & Hatzilygeroudis, I. (2014) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale.2014.7062571 · Cited by 38
Toward an Open Learning Analytics Ecosystem
Chatti, M. A., Muslim, A., & Schroeder, U. (2016) · Springer eBooks · doi:10.1007/978-3-319-06520-5_12 · Cited by 38
Sun, G., Cui, T., Beydoun, G., Chen, S., Dong, F., Dongming Xu, A., & Shen, J. (2017) · Sustainability · doi:10.3390/su9060898 · Cited by 38
A Survey on Educational Data Mining [2014-2019]
Aberbach, H., Jeghal, A., Sabri, M. A., & Tairi, H. (2020) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/iscv49265.2020.9204013 · Cited by 38
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Wassan, J. T. (2015) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2015.01.522 · Cited by 38
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Hossain, Z., Jin, X., Bumbacher, E. W., Chung, A. M., Koo, S., Shapiro, J. D., Truong, C., Choi, S., Orloff, N. D., Blikstein, P., et al. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702354 · Cited by 36
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Moscoso-Zea, O., Paredes-Gualtor, J., & Luján‐Mora, S. (2018) · IEEE Access · doi:10.1109/access.2018.2876753 · Cited by 37
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Aleem, A., & Gore, M. M. (2020) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt48778.2020.9115734 · Cited by 40
Goggins, S., Galyen, K., Petakovic, E., & Laffey, J. M. (2016) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12129 · Cited by 44
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Conde, M. Á., García‐Peñalvo, F. J., Fidalgo-Blanco, Á., & Séin-Echaluce, M. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58515-4_19 · Cited by 36
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Eckert, K. B., & Suénaga, R. (2015) · Formación universitaria · doi:10.4067/s0718-50062015000500002 · Cited by 39
Nakamura, C. M., Murphy, S. K., Christel, M. G., Stevens, S., & Zollman, D. A. (2016) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.12.010122 · Cited by 38
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Tieu, B. H., & Duy, B. T. (2017) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2017.8119433 · Cited by 36
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Olabanjo, O. A., Wusu, A. S., & Manuel, M. (2022) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2022.100190 · Cited by 47
Predicting Facial Indicators of Confusion with Hidden Markov Models
Grafsgaard, J. F., Boyer, K., & Lester, J. C. (2011) · Affective Computing and Intelligent Interaction · doi:10.1007/978-3-642-24600-5_13 · Cited by 48
Learning Analytics and Educational Data Mining
Baker, R. S., & Siemens, G. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108888295.016 · Cited by 39
Student Modeling Based on Problem Solving Times
Pelánek, R., & Jarušek, P. (2015) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0048-x · Cited by 35
Cobo, Á., Blanco, R. R., & Hoyos, C. R. (2013) · Behaviour and Information Technology · doi:10.1080/0144929x.2013.853838 · Cited by 35
Bouvier, P., Lavoué, É., Sehaba, K., & George, S. (2013) · International Conference on Computer Supported Education · doi:10.5220/0004386903390350 · Cited by 38
Datawarehouse design for educational data mining
Moscoso-Zea, O., Andres-Sampedro, & Luján‐Mora, S. (2016) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet.2016.7760754 · Cited by 35
Poitras, E., Doleck, T., Huang, L., Li, S., & Lajoie, S. P. (2017) · Australasian Journal of Educational Technology · doi:10.14742/ajet.3498 · Cited by 42
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Zhang, J., Zhang, X., Jiang, S., de Pablos, P. O., & Sun, Y. (2018) · Behaviour and Information Technology · doi:10.1080/0144929x.2018.1529198 · Cited by 35
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Xu, D., & Xu, Y. (2020) · Higher education · doi:10.1007/978-3-030-31365-4_10 · Cited by 39
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Тимофеев, А. В., Vanhoof, K., Волошин, А. Д., Eremeev, A., Aslanyan, L. H., Kleshchev, A., López, L. F. D. M., Palagin, A. V., Mintchev, M. P., Milani, A., et al. (2009) · Cited by 35
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Borges, V. R. P., Esteves, S., Araújo, P. D. N., de Oliveira, L. C., & de Holanda, M. T. (2018) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/cbie.sbie.2018.1383 · Cited by 34
Doleck, T., Basnet, R. B., Poitras, E., & Lajoie, S. P. (2015) · Journal of Computers in Education · doi:10.1007/s40692-015-0040-3 · Cited by 34
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EDUCATIONAL DATA MINING APPLICATIONS
Prabha, S. L. (2014) · Cited by 34
Insights into the adoption of social media mashups
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Educational data mining acceptance among undergraduate students
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RetractedManhães, L. M. B., da Cruz, S. M. S., & Zimbrão, G. (2014) · ACM Symposium on Applied Computing · doi:10.1145/2554850.2555135 · Cited by 32
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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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