Adaptive Learning Service Based on Spacing Effect

2022;
: cc. 91 - 100
Автори:
1
Lviv Polytechnic National University, Ukraine

 In the article, the adaptive educational service is based on the mechanism of interval repetitions. This system allows the user to study material products without much effort. The technology "Training with reinforcement" has been used as a mechanism of interval repetitions. The technology and an adaptive service of development environment have been reasonably chosen. The structural scheme, the scheme of the algorithm of work, and the scheme of the database structure have been developed. The program has been implemented using the C# programming language and using ASP.NET technologies and its library.

The purpose of the study: to develop an adaptive learning service based on the technology of interval repetition.

  1. Kanyin F., Xiao Z., Jing L., Ying C., Zhifang Y, Chuansheng C., Gui X. (2019). Journal of Neuroscience. pp. 25–­39. DOI: 10.1523/JNEUROSCI.2741-18.2019
  2. Zheng L, Gao Z, Xiao X, Ye Z, Chen C, Xue G. (2018). Reduced fidelity of neural representation underlies episodic memory decline in normal aging. pp. 1020–1022 DOI: 10.1093/cercor/bhx130
  3. Siegel, L. L., Kahana, M. J. (2014). A retrieved context account of spacing and repetition effects in free recall. J. Exp. Psychol. Learn. Mem. Cogn. 40. pp. 755–764. DOI: 10.1037/a0035585
  4. APS.NET service [Electronic resource] - Resource access mode: https://dotnet.microsoft.com/apps/aspnet. (Accessed: 02/22/2022)
  5. Bourne, J. N., Harris, K. M. (2011). The coordination of size and number of excitatory and inhibitory synapses results in balanced structural plasticity along mature hippocampal CA1 dendrites during LTP. Hippocampus 21. pp. 354-363. DOI: 10.1002/hipo.20768
  6. Botchkaryov A. (2020). The decentralized control of adaptive data collection processes based on equilibrium concept and reinforcement learning // Advances in Cyber-Physical Systems, Lviv, Volume 5, Number 2, pp. 50–55. DOI: 10.23939/acps2020.02.050.
  7. Radvansky G. (2021). Human Memory. pp. 451–453. DOI: 10.4324/9780429287039
  8. Botchkaryov A. (2016). Organization of adaptive processes of information collection in mobile cyber-physical systems, Proceedings of the Second Scientific Seminar “Cyber-physical Systems: Achievements and Challenges”, Lviv Polytechnic National University, pp. 62–67. DOI: DOI: 10.23939/csn2020.01.027
  9. Bhuvan Unhelkar (2018). Software Engineering with UML, pp. 360–362. DOI: 10.1201/9781351235181
  10. Richard S. Sutton, Andrew G. Barto. (2018). Reinforcement Learning: An Introduction. The MIT Press. pp. 257–360. DOI: 10.3156/jsoft.21.214