Explainable Prediction of Student Performance in Online Courses
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-41637-8_52
Reference25 articles.
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2. Celik, I., Gedrimiene, E., Silvola, A., Muukkonen, H.: Response of learning analytics to the online education challenges during pandemic: opportunities and key examples in higher education. Policy Fut. Educ. (in press, 2022)
3. Tomasevic, N., Gvozdenovic, N., Vranes, S.: An overview and comparison of supervised data mining techniques for student exam performance prediction. Comput. Educ. 143, 1–18 (2020)
4. Du, X., Yang, J., Hung, J.: An integrated framework based on latent variational autoencoder for providing early warning of at-risk students. IEEE Access 8, 10110–10122 (2020)
5. Capuano, N., Lomasto, L., Pozzi, A., Toti, D.: Natural language understanding for the recommendation of learning resources within student collaboration tools. In: Proceedings of the Learning Ideas Conference, New York (2022)
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