Online Performance Prediction Combined Prior Knowledge and Deep Learning Models
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Publisher
Springer Nature Singapore
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https://link.springer.com/content/pdf/10.1007/978-981-97-4243-1_9
Reference20 articles.
1. Zhang, W., Huang, X., Wang, S., Shu, J., Liu, H., Chen, H.: Student performance prediction via online learning behavior analytics. In Proceedings of the 2017 International Symposium on Educational Technology (ISET), Hong Kong, China, pp. 153–157 (2017). https://doi.org/10.1109/ISET.2017.43, https://ieeexplore.ieee.org/document/8005410
2. Zhou, Q., Zheng, Y., Mou, C.: Predicting students’ performance of an offline course from their online behaviors. In: Proceedings of the 2015 Fifth International Conference on Digital Information and Communication Technology and Its Applications (DICTAP),Beirut, Lebanon, 29 April–1 May 2015, pp. 70–73 (2015). https://doi.org/10.1109/DICTAP.2015.7113173, https://ieeexplore.ieee.org/document/7113173
3. Figueroa-Canas, J., Sancho-Vinuesa, T.: Early prediction of dropout and final exam performance in an online statistics course. IEEE Rev. Iberoam. Tecnol. Aprendiz. 15, 86–94 (2020). https://doi.org/10.1109/RITA.2020.2987727, https://ieeexplore.ieee.org/abstract/document/9064814
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5. Von Rueden, L., Mayer, S., Beckh, K., et al.: Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans. Knowl. Data Eng. 35(1), 614–633 (2021). https://doi.org/10.1109/TKDE.2021.3079836, https://arxiv.org/pdf/1903.12394.pdf
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