Adaptive Learning Service Based on Spacing Effect

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dc.citation.epage100
dc.citation.issue2
dc.citation.spage91
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorDudok, B.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-03-19T10:17:57Z
dc.date.available2024-03-19T10:17:57Z
dc.date.created2022-02-28
dc.date.issued2022-02-28
dc.description.abstractIn 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.
dc.format.extent91-100
dc.format.pages10
dc.identifier.citationDudok B. Adaptive Learning Service Based on Spacing Effect / B. Dudok // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 7. — No 2. — P. 91–100.
dc.identifier.citationenDudok B. Adaptive Learning Service Based on Spacing Effect / B. Dudok // Advances in Cyber-Physical Systems. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 7. — No 2. — P. 91–100.
dc.identifier.doidoi.org/10.23939/acps2022.02.091
dc.identifier.issn2524-0382
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61494
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofAdvances in Cyber-Physical Systems, 2 (7), 2022
dc.relation.references[1] Kanyin F., Xiao Z., Jing L., Ying C., Zhifang Y, Chuansheng C., Gui X. (2019). Journal of Neuroscience, 25–39. DOI: 10.1523/JNEUROSCI.2741-18.2019
dc.relation.references[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, 1020–1022. DOI: 10.1093/cercor/bhx130
dc.relation.references[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, 755–764. DOI: 10.1037/a0035585
dc.relation.references[4] APS.NET service [Electronic resource]. Resource access mode: https://dotnet.microsoft.com/apps/aspnet (Accessed: 02/22/2022)
dc.relation.references[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, 354–363. DOI: 10.1002/hipo.20768
dc.relation.references[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, Vol. 5, No. 2, 50–55. DOI: 10.23939/acps2020.02.050.
dc.relation.references[7] Radvansky G. (2021). Human Memory, 451–453. DOI: 10.4324/9780429287039
dc.relation.references[8] Botchkaryov A. (2016). Organization of adaptive processes of information collection in mobile cyber-physical systems, Proceedings of the Second Scientific Seminar “Cyberphysical Systems: Achievements and Challenges”, Lviv Polytechnic National University, pp. 62–67. DOI: 10.23939/csn2020.01.027
dc.relation.references[9] Bhuvan Unhelkar (2018). Software Engineering with UML, pp. 360–362. DOI: 10.1201/9781351235181
dc.relation.references[10] Richard S. Sutton, Andrew G. Barto (2018). Reinforcement Learning: An Introduction. The MIT Press, 257–360. DOI: 10.3156/jsoft.21.214
dc.relation.referencesen[1] Kanyin F., Xiao Z., Jing L., Ying C., Zhifang Y, Chuansheng C., Gui X. (2019). Journal of Neuroscience, 25–39. DOI: 10.1523/JNEUROSCI.2741-18.2019
dc.relation.referencesen[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, 1020–1022. DOI: 10.1093/cercor/bhx130
dc.relation.referencesen[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, 755–764. DOI: 10.1037/a0035585
dc.relation.referencesen[4] APS.NET service [Electronic resource]. Resource access mode: https://dotnet.microsoft.com/apps/aspnet (Accessed: 02/22/2022)
dc.relation.referencesen[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, 354–363. DOI: 10.1002/hipo.20768
dc.relation.referencesen[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, Vol. 5, No. 2, 50–55. DOI: 10.23939/acps2020.02.050.
dc.relation.referencesen[7] Radvansky G. (2021). Human Memory, 451–453. DOI: 10.4324/9780429287039
dc.relation.referencesen[8] Botchkaryov A. (2016). Organization of adaptive processes of information collection in mobile cyber-physical systems, Proceedings of the Second Scientific Seminar "Cyberphysical Systems: Achievements and Challenges", Lviv Polytechnic National University, pp. 62–67. DOI: 10.23939/csn2020.01.027
dc.relation.referencesen[9] Bhuvan Unhelkar (2018). Software Engineering with UML, pp. 360–362. DOI: 10.1201/9781351235181
dc.relation.referencesen[10] Richard S. Sutton, Andrew G. Barto (2018). Reinforcement Learning: An Introduction. The MIT Press, 257–360. DOI: 10.3156/jsoft.21.214
dc.relation.urihttps://dotnet.microsoft.com/apps/aspnet
dc.rights.holder© Національний університет “Львівська політехніка”, 2022
dc.rights.holder© Dudok B., 2022
dc.subjecttraining service
dc.subjectinterval repetition
dc.subjectadaptive service
dc.titleAdaptive Learning Service Based on Spacing Effect
dc.typeArticle

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Advances In Cyber-Physical Systems. – 2022. – Vol. 7, No. 2