The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Web usage mining for predicting final marks of students that use Moodle courses
Abstract
This paper shows how web usage mining can be applied in e‐learning systems in order to predict the marks that university students will obtain in the final exam of a course. We have also developed a specific Moodle mining tool oriented for the use of not only experts in data mining but also of newcomers like instructors and courseware authors. The performance of different data mining techniques for classifying students are compared, starting with the student's usage data in several Cordoba University Moodle courses in engineering. Several well‐known classification methods have been used, such as statistical methods, decision trees, rule and fuzzy rule induction methods, and neural networks. We have carried out several experiments using all available and filtered data to try to obtain more accuracy. Discretization and rebalance pre‐processing techniques have also been used on the original numerical data to test again if better classifier models can be obtained. Finally, we show examples of some of the models discovered and explain that a classifier model appropriate for an educational environment has to be both accurate and comprehensible in order for instructors and course administrators to be able to use it for decision making. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 135–146, 2013
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Cited by
Romero, C., & Ventura, S. (2013) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1075 · Cited by 869
Self-citationOuyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2019.01.007 · Cited by 564
Predicting academic performance: a systematic literature review
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018) · Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3293881.3295783 · Cited by 355
Early dropout prediction using data mining: a case study with high school students
Márquez‐Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016) · Expert Systems · doi:10.1111/exsy.12135 · Cited by 323
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Metadata
- Authors
- Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S.
- Venue
- Computer Applications in Engineering Education
- Publisher
- Wiley
- Published
- 2013-01-18
- Volume
- 21
- Issue
- 1
- Pages
- 135-146
- ISSN
- 1061-3773 (ISSN-L)
- 1099-0542
- OpenAlex
- W2103672843
- Cited by
- 288
- References
- 59
- FWCI
- 15.60
- Type
- journal article
- Language
- en
- Open access
- closed
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