The DOI matches its Crossref record.
Using machine learning to predict student difficulties from learning session data
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Citations
Cited by
Enhancing the prediction of student performance based on the machine learning XGBoost algorithm
Asselman, A., Khaldi, M., & Aammou, S. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2021.1928235 · Cited by 490
Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/6347186 · Cited by 382
Self-citationAnalyzing 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
A systematic review: machine learning based recommendation systems for e-learning
Khanal, S. S., Prasad, P. W. C., Alsadoon, A., & Maag, A. (2020) · Education and Information Technologies · doi:10.1007/s10639-019-10063-9 · Cited by 359
Chen, X., Xie, H., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100005 · Cited by 303
Metadata
- Authors
- Hussain, M., Zhu, W., Zhang, W., Abidi, S. M. R., & Ali, S.
- Venue
- Artificial Intelligence Review
- Published
- 2018-02-10
- Volume
- 52
- Issue
- 1
- Pages
- 381-407
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2793183907
- Cited by
- 249
- References
- 0
- FWCI
- 24.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Using machine learning to predict student difficulties from learning session data. https://app.cochat.ai/papers/10.1007/s10462-018-9620-8
- The paper
- Hussain, M., Zhu, W., Zhang, W., Abidi, S. M. R., & Ali, S. (2019). Using machine learning to predict student difficulties from learning session data. Artificial Intelligence Review. https://doi.org/10.1007/s10462-018-9620-8
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.
