Author:
Arkiza Mariam,Hakkal Soukaina,Oumaira Ilham,Ait Lahcen Ayoub
Publisher
Springer Nature Switzerland
Reference45 articles.
1. Fasihuddin, H.A., Skinner, G.D., Athauda, R.I.: Boosting the opportunities of open learning (MOOCs) through learning theories. GSTF J. Comput. (JoC) 3(3), 1–6 (2013). https://doi.org/10.7603/s40601-013-0031-z
2. Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., Navarro-Colorado, B.: A systematic review of deep learning approaches to educational data mining. Complexity 2019, e1306039 (2019). https://doi.org/10.1155/2019/1306039
3. Liu, D., Zhang, Y., Zhang, J., Li, Q., Zhang, C., Yin, Y.: Multiple features fusion attention mechanism enhanced deep knowledge tracing for student performance prediction. IEEE Access 8, 194894–194903 (2020). https://doi.org/10.1109/ACCESS.2020.3033200
4. Al-Jarrah, O.Y., Yoo, P.D., Muhaidat, S., Karagiannidis, G.K., Taha, K.: Efficient machine learning for big data: a review. Big Data Res. 2, 87–93 (2015). https://doi.org/10.1016/j.bdr.2015.04.001
5. Li, J.: Evaluating the Effects of BKT-LSTM on Students’ Learning Performance (2021). https://hammer.purdue.edu/articles/thesis/Evaluating_the_Effects_of_BKT-LSTM_on_Students_Learning_Performance/17126693/1
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. XGBoost To Enhance Learner Performance Prediction;Computers and Education: Artificial Intelligence;2024-12