Student Performance Prediction Model Based on Course Description and Student Similarity
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61003-5_9
Reference15 articles.
1. Arifin, M., Widowati, W., Farikhin, F., Gudnanto, G.: A regression model and a combination of academic and non-academic features to predict student academic performance. TEM J. 12(2), 855–864 (2023). https://doi.org/10.18421/TEM122-31
2. Cheng, B., Liu, Y., Jia, Y.: Evaluation of students’ performance during the academic period using the XG-boost classifier-enhanced AEO hybrid model. Expert Syst. Appl. 238, 122136 (2024). https://doi.org/10.1016/j.eswa.2023.122136
3. Issah, I., Appiah, O., Appiahene, P., Inusah, F.: A systematic review of the literature on machine learning application of determining the attributes influencing academic performance. Decis. Anal. J. 7(October 2022), 100204 (2023). https://doi.org/10.1016/j.dajour.2023.100204
4. Khudhur, A., Ramaha, N.T.: Students’ performance prediction using machine learning based on generative adversarial network. In: 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1–6. IEEE (2023). https://doi.org/10.1109/HORA58378.2023.10156733
5. Li, M., Zhang, Y., Li, X., Cai, L., Yin, B.: Multi-view hypergraph neural networks for student academic performance prediction. Eng. Appl. Artif. Intell. 114(June), 105174 (2022). https://doi.org/10.1016/j.engappai.2022.105174
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