The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Analyzing undergraduate students' performance using educational data mining
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Cites
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
Pelleg, D., & Moore, A. (2000) · Cited by 2,245
The 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
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
International encyclopedia of education
Peterson, P. L. (2010) · Cited by 728
Predicting students' final performance from participation in on-line discussion forums
Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.06.009 · Cited by 546
Huang, S., & Fang, N. (2013) · Computers & Education · doi:10.1016/j.compedu.2012.08.015 · Cited by 373
Predicting Student Performance by Using Data Mining Methods for Classification
Kabakchieva, D. (2013) · Cybernetics and Information Technologies · doi:10.2478/cait-2013-0006 · Cited by 299
A comparative analysis of techniques for predicting academic performance
Nghe, N. T., Janecek, P., & Haddawy, P. (2007) · 2007 37th annual frontiers in education conference - global engineering: knowledge without borders, opportunities without passports · doi:10.1109/fie.2007.4417993 · Cited by 240
Cited by
Yağcı, M. (2022) · Smart Learning Environments · doi:10.1186/s40561-022-00192-z · Cited by 698
Predicting academic success in higher education: literature review and best practices
Alyahyan, E., & Düştegör, D. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-0177-7 · Cited by 615
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-022-00372-4 · Cited by 387
Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. V. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2018.02.012 · Cited by 316
Scalable Computing: Practice and Experience
(2013) · Scalable Computing Practice and Experience · doi:10.12694/issn.1895-1767 · Cited by 280
Metadata
- Authors
- Asif, R., Merceron, A., Ali, S. A., & Haider, N. G.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 113
- Pages
- 177-194
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2617300785
- Cited by
- 586
- References
- 21
- FWCI
- 61.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & Education. https://doi.org/10.1016/j.compedu.2017.05.007
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.
