The DOI matches its Crossref record.
Predicting Student Performance by Using Data Mining Methods for Classification
Open accessAbstract
Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.
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Cited by
Analyzing undergraduate students' performance using educational data mining
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.05.007 · Cited by 586
Predicting Student Success in College Algebra Classes Using Machine Learning
Akcay Ozkan, Z., Yu, Y., & Stelmach, E. (2024) · 2023 ASEE Annual Conference & Exposition Proceedings · doi:10.18260/1-2--43933
Metadata
- Authors
- Kabakchieva, D.
- Venue
- Cybernetics and Information Technologies
- Publisher
- Walter de Gruyter GmbH
- Published
- 2013-03-01
- Volume
- 13
- Issue
- 1
- Pages
- 61-72
- ISSN
- 1311-9702 (ISSN-L)
- 1314-4081
- OpenAlex
- W2041304617
- Cited by
- 299
- References
- 0
- FWCI
- 19.19
- Type
- journal article
- Language
- en
- Open access
- diamond
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- The paper
- Kabakchieva, D. (2013). Predicting Student Performance by Using Data Mining Methods for Classification. Cybernetics and Information Technologies. https://doi.org/10.2478/cait-2013-0006
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.
