An early warning system to predict dropouts inside e-learning environments
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10639-024-12498-1.pdf
Reference24 articles.
1. Aggarwal, D., Mittal, S., & Bali, V. (2021). Significance of non-academic parameters for predicting student performance using ensemble learning techniques. International Journal of System Dynamics Applications, 10(3), 38–49. https://doi.org/10.4018/ijsda.2021070103.
2. AL-Malaise, A., Malibari, A., & Alkhozae, M. (2014). Students performance prediction system using multi agent data mining technique. International Journal of Data Mining & Knowledge Management Process, 4(5), 01–20. https://doi.org/10.5121/ijdkp.2014.4501.
3. Baneres, D., Rodriguez, M. E., & Serra, M. (2019). An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Transactions on Learning Technologies, 12(2), 249–263. https://doi.org/10.1109/TLT.2019.2912167.
4. Boudjehem, R., & Lafifi, Y. (2021). A new approach to identify dropout learners based on their performance-based behavior. JUCS - Journal of Universal Computer Science, 27(10), 1001–1025. https://doi.org/10.3897/jucs.74280.
5. Howard, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. The Internet and Higher Education, 37, 66–75. https://doi.org/10.1016/j.iheduc.2018.02.001.
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