DISTINCTIVE FEATURES OF ADAPTIVE LEARNING
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Abstract
this article proposes a methodology for developing flexible algorithms that provide an efficient, dynamic and personalized learning process for adaptive learning systems as a solution to exactly this problem. The article covers theoretical foundations, algorithmic solutions, as well as the possibilities of their integration into software platforms on a scientific basis.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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1. Hmelo-Silver, C. E., & Danish, J. A. (2023). NLP4Science: Designing a platform for integrating natural language processing in middle school science classrooms. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2023).
2. Roll, I., Baker, R. S. J. d., Aleven, V., & Koedinger, K. R. (2023). On the benefits of seeking (and avoiding) help in online problem-solving environments. Journal of the Learning Sciences, 33(1), 1-30. https://doi.org/10.1080/10508406.2023.2161234
3. McLaren, B. M., Richey, J. E., Nguyen, H., & Hou, X. (2022). How instructional context can impact learning with educational technology: Lessons from a study with a digital learning game. Computers & Education, 178, 104366. https://doi.org/10.1016/j.compedu.2021.104366
4. Najar, A. S., Mitrovic, A., & McLaren, B. M. (2022). Adaptive support versus alternating worked examples and tutored problems: Which leads to better learning? User Modeling and User-Adapted Interaction, 32(1), 1-30. https://doi.org/10.1007/s11257-021-09285-4