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PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES
Open accessAbstract
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level distance learning. Students' key demographic characteristics and their marks on a few written assignments can constitute the training set for a supervised machine learning algorithm. The learning algorithm could then be able to predict the performance of new students, thus becoming a useful tool for identifying predicted poor performers. The scope of this work is to compare some of the state of the art learning algorithms. Two experiments have been conducted with six algorithms, which were trained using data sets provided by the Hellenic Open University. Among other significant conclusions, it was found that the Naïve Bayes algorithm is the most appropriate to be used for the construction of a software support tool, has more than satisfactory accuracy, its overall sensitivity is extremely satisfactory, and is the easiest algorithm to implement.
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Cited by
Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/6347186 · Cited by 382
Practical early prediction of students’ performance using machine learning and eXplainable AI
Jang, Y., Choi, S., Jung, H., & Kim, H. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11120-6 · Cited by 114
A review of machine learning methods used for educational data
Ersozlu, Z., Taheri, S., & Koch, I. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12704-0 · Cited by 96
“This is because ML methods have consistently outperformed traditional methods on training data, achieving higher levels of accuracy, and generalising better across diverse datasets (Japkowicz & Shah, 2011; Kotsiantis et al., 2004).”
A systematic review on machine learning models for online learning and examination systems
Kaddoura, S., Popescu, D. E., & Hemanth, J. D. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.986 · Cited by 75
Metadata
- Authors
- KOTSIANTIS, S., PIERRAKEAS, C., & PINTELAS, P.
- Venue
- Applied Artificial Intelligence
- Publisher
- Informa UK Limited
- Published
- 2004-05
- Volume
- 18
- Issue
- 5
- Pages
- 411-426
- ISSN
- 0883-9514 (ISSN-L)
- 1087-6545
- OpenAlex
- W2038749650
- Cited by
- 330
- References
- 0
- FWCI
- 9.88
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES. https://app.cochat.ai/papers/10.1080/08839510490442058
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- KOTSIANTIS, S., PIERRAKEAS, C., & PINTELAS, P. (2004). PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES. Applied Artificial Intelligence. https://doi.org/10.1080/08839510490442058
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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.
