The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Data mining in course management systems: Moodle case study and tutorial
Abstract
Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.
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Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
Cited by
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-citationEducational data mining and learning analytics: An updated survey
Romero, C., & Ventura, S. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1355 · Cited by 1,120
Self-citationApplication 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
Romero, C., & Ventura, S. (2013) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1075 · Cited by 869
Self-citationA reference model for learning analytics
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2012.051815 · Cited by 702
Metadata
- Authors
- Romero, C., Ventura, S., & García, E.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2008-08
- Volume
- 51
- Issue
- 1
- Pages
- 368-384
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2079777076
- Cited by
- 1,058
- References
- 2
- FWCI
- 77.06
- Type
- journal article
- Language
- en
- Open access
- closed
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- Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education. https://doi.org/10.1016/j.compedu.2007.05.016
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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.
