The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Discovering Statistics Using SPSS
Field, A. P., & Miles, J. N. V. (2000) · Medical Entomology and Zoology · Cited by 28,265
Cohen, L., Manion, L., & Morrison, K. (2007) · Routledge eBooks · doi:10.4324/9780203029053 · Cited by 20,105
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Motivational and self-regulated learning components of classroom academic performance.
Pintrich, P. R., & De Groot, E. V. (1990) · Journal of Educational Psychology · doi:10.1037/0022-0663.82.1.33 · Cited by 8,332
The Nature of Statistical Learning Theory
Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434
Forecasting with artificial neural networks:
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,308
Choosing Multiple Parameters for Support Vector Machines
Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002) · Machine Learning · doi:10.1023/a:1012450327387 · Cited by 2,206
Cited by
Yağcı, M. (2022) · Smart Learning Environments · doi:10.1186/s40561-022-00192-z · Cited by 698
(2014) · Educational Technology & Society · Cited by 598
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
Identifying significant indicators using LMS data to predict course achievement in online learning
You, J. W. (2016) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.11.003 · Cited by 388
Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020) · Applied Sciences · doi:10.3390/app10031042 · Cited by 367
Metadata
- Authors
- Huang, S., & Fang, N.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 61
- Pages
- 133-145
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2145445683
- Cited by
- 373
- References
- 35
- FWCI
- 12.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. https://app.cochat.ai/papers/10.1016/j.compedu.2012.08.015
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- Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education. https://doi.org/10.1016/j.compedu.2012.08.015
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.
