The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Educational data mining and learning analytics: An updated survey
Open accessAbstract
This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area. This article is categorized under: Application Areas > Education and Learning
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Self-citationPenetrating the Fog: Analytics in Learning and Education.
Siemens, G., & Long, P. (2011) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 1,752
Educational data mining: A survey from 1995 to 2005
Romero, C., & Ventura, S. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.04.005 · Cited by 1,482
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 1,359
Learning analytics: drivers, developments and challenges
Ferguson, R. (2012) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2012.051816 · Cited by 1,354
Learning Analytics: The Emergence of a Discipline
Siemens, G. (2013) · American Behavioral Scientist · doi:10.1177/0002764213498851 · Cited by 1,284
Learning analytics and educational data mining: towards communication and collaboration
Siemens, G., & Baker, R. S. J. D. (2012) · Proceedings of the 2nd International Conference on Learning Analytics and Knowledge · doi:10.1145/2330601.2330661 · Cited by 1,075
Data mining in course management systems: Moodle case study and tutorial
Romero, C., Ventura, S., & García, E. (2008) · Computers & Education · doi:10.1016/j.compedu.2007.05.016 · Cited by 1,058
Self-citationEducational Data Mining and Learning Analytics
Baker, R. S., & Inventado, P. S. (2014) · Learning Analytics · doi:10.1007/978-1-4614-3305-7_4 · Cited by 1,029
Course signals at Purdue: using learning analytics to increase student success
Arnold, K. E., & Pistilli, M. D. (2012) · Proceedings of the 2nd International Conference on Learning Analytics and Knowledge · doi:10.1145/2330601.2330666 · Cited by 987
Cited by
Namoun, A., & Alshanqiti, A. (2020) · Applied Sciences · doi:10.3390/app11010237 · Cited by 473
Automatic feedback in online learning environments: A systematic literature review
Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.-S., Gašević, D., & Mello, R. F. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100027 · Cited by 440
Artificial intelligence-enabled adaptive learning platforms: A review
Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100429 · Cited by 207
AI-assisted knowledge assessment techniques for adaptive learning environments
Minn, S. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100050 · Cited by 166
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
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
Smart Environment for Adaptive Learning of Cybersecurity Skills
Vykopal, J., Seda, P., Švábenský, V., & Čeleda, P. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3216345 · Cited by 48
Backward evaluation in peer assessment: A scoping review
Misiejuk, K., & Wasson, B. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104319 · Cited by 46
Personalisation methods in e-learning-A literature review
Mikić, V., Ilić, M., Kopanja, L., & Vesin, B. (2022) · Computer Applications in Engineering Education · doi:10.1002/cae.22566 · Cited by 39
Dobashi, K., Ho, C. P., Fulford, C. P., Grace Lin, M.-F., & Higa, C. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100105 · Cited by 35
Misiejuk, K., López-Pernas, S., Kaliisa, R., & Saqr, M. (2025) · Journal of Learning Analytics · doi:10.18608/jla.2025.8591 · Cited by 30
Shin, I., Hwang, S. B., Yoo, Y. J., Bae, S., & Kim, R. Y. (2025) · Proceedings of the 15th International Learning Analytics and Knowledge Conference · doi:10.1145/3706468.3706488 · Cited by 16
“Learning Analytics refers to the data-driven process of measuring, gathering, analyzing, and reporting on learners and educational contexts to better understand and enhance both learning and the environments where it takes place [22, 32].”
Metadata
- Authors
- Romero, C., & Ventura, S.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2020-01-13
- Volume
- 10
- Issue
- 3
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W3000065748
- Cited by
- 1,120
- References
- 60
- FWCI
- 113.54
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
