A Rule-Based Adaptive Mobile Application to Learn Android in a Personalized Learning Environment
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-3590-9_17
Reference15 articles.
1. Ahmed, M. U., & Sangi, N. A. (2017). A learner model for adaptable e-learning. International Journal of Advanced Computer Science and Applications (IJACSA), 8(6), 139–147. https://doi.org/10.14569/IJACSA.2017.080618
2. Bradac, V., & Walek, B. (2017). A comprehensive adaptive system for e-learning of foreign languages. Expert Systems with Applications, 90, 414–426. https://doi.org/10.1016/j.eswa.2017.08.019
3. Cai, L., Barnes, L.E., Boukhechba, M. (2022). A framework for adaptive mobile ecological momentary assessments using reinforcement learning. In: K. Arai (Ed.) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems (Vol. 294). Springer. https://doi.org/10.1007/978-3-030-82193-7_3
4. Cheng, J., & Wang, H. (2021). Adaptive algorithm recommendation and application of learning resources in English fragmented reading. Complexity, 2021, Article ID 5592534. https://doi.org/10.1155/2021/5592534
5. Diao, H., Lu, Y., Deng, A., Zou, Li., Li, X., & Pedrycz, W. (2022). Convolutional rule inference network based on belief rule-based system using an evidential reasoning approach. Knowledge-Based Systems, 237, 107713. ISSN 0950-7051
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