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Методика використання інформаційно-когнітивних технологій адаптивного навчання студентів педагогічних університетів
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| dc.contributor.author | Фадєєва, Лілія Олександрівна | |
| dc.date.accessioned | 2024-06-03T08:33:46Z | |
| dc.date.available | 2024-06-03T08:33:46Z | |
| dc.date.issued | 2024 | |
| dc.identifier.citation | Фадєєва Л. О. Методика використання інформаційно-когнітивних технологій адаптивного навчання студентів педагогічних університетів : дисертація на здобуття наукового ступеня доктора філософії за спеціальністю 011 Освітні, педагогічні науки / наук. керівник - доктор педагогічних наук, професор С. О. Семеріков ; Криворізький державний педагогічний університет. Кривий Ріг, 2024. 155 с. | uk |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/10189 | |
| dc.description | [1] Abuzinadah, N., Umer, M., Ishaq, A., Hejaili, A.A., Alsubai, S., Eshmawi, A.A., Mohamed, A. and Ashraf, I., 2023. Role of convolutional features and machine learning for predicting student academic performance from MOODLE data. PLoS ONE. Public Library of Science, vol. 18. URL https://doi.org/10.1371/journal.pone. 0293061. [2] Afini Normadhi, N.B., Shuib, L., Md Nasir, H.N., Bimba, A., Idris, N. and Balakrishnan, V., 2019. Identification of personal traits in adaptive learning environment: Systematic literature review. Computers and Education, 130, pp.168–190. URL https://doi.org/10.1016/j.compedu.2018.11.005. [3] Aguar, K.E., 2018. Sail: a system for adaptive interest-based learning in stem education. PhD thesis. University of Georgia. URL http://hdl.handle.net/10724/37603. [4] Ako-Nai, F., Tan, Q., Pivot, F.C. and Kinshuk, 2012. The 5R adaptive learning content generation platform for mobile learning. Proceedings - 2012 IEEE 4th International Conference on Technology for Education, T4E 2012. pp.132–137. URL https://doi.org/10.1109/T4E.2012.22. [5] Albert, D. and Steiner, C.M., 2011. Reflections on the evaluation of adaptive learning technologies. Proceedings - IEEE International Conference on Technology for Education, T4E 2011. pp.295–296. URL https://doi.org/10.1109/T4E.2011.62. [6] Aleven, V., McLaughlin, E., Glenn, R.A. and Koedinger, K.R., 2016. Instruction based on adaptive learning technologies. Handbook of Research on Learning and Instruction, Second edition. Taylor and Francis, pp.522–559. URL https://doi.org/ 10.4324/9781315736419-33. [7] Aljojo, N., Adams, C., Saifuddin, H. and Alsehaimi, Z., 2011. Evaluating the impact of an Arabic version of an adaptive learning system based on the felder-silverman’s learning style instrument. In: A. Rospigliosi and S. Greener, eds. Proceedings of the European Conference on Games-based Learning. Dechema e.V., vol. 2, pp.897–908. URL https://www.academia.edu/6160042. [8] Allison, G.L. and Extavour, R.M., 2017. Pharmacy Students’ Perceptions and Usage of an Adaptive Learning Technology (SmartBook®) in Anatomy and Physiology in a Caribbean School of Pharmacy. Ubiquitous Learning, 10(3), pp.1–9. URL https://doi.org/10.18848/1835-9795/CGP/v10i03/1-9. [9] Ananta, I.G.P., 2004. Using an adaptive web-based learning environment to develop conceptual and procedural knowledge. PhD thesis. University of Wollongong. URL https://ro.uow.edu.au/theses/202. [10] Anderman, E.M. and Midgley, C., 2014. Methods for Studying Goals, Goal Structures, and Patterns of Adaptive Learning. In: R.W. Roeser, R. Marachi and H. Gehlbach, eds. Goals, Goal Structures, and Patterns of Adaptive Learning. Taylor and Francis, pp.1–20. URL https://doi.org/10.4324/9781410602152-8. [11] Andrukhiv, A.I., 2015. Methods and means of adaptive support of university learning process with library materials. Thesis for a Ph.D degree in specialty 01.05.03 – mathematical and software development for computer systems. Lviv Polytechnic National University, Lviv. URL https://nrat.ukrintei.ua/searchdoc/0415U005842. [12] Angelaccio, M. and Buttarazzi, B., 2011. EduSHARE®: A peer to peer document sharing system for obtaining adaptive learning procedures. International Journal of Technology Enhanced Learning, 3(5), pp.525–535. URL https://doi.org/10.1504/ IJTEL.2011.042103. [13] Bai, P., Zhang, X., Dai, F., Wang, J. and Wang, X., 2018. Research on the Adaptive Learning Support System of Air Traffic Control. In: X. Wang, Y. Zhang, D. Yang and Z. You, eds. CICTP 2018: Intelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals. American Society of Civil Engineers (ASCE), pp.2234–2245. URL https://doi.org/10.1061/ 9780784481523.222. [14] Basitere, M. and Ivala, E., 2017. Evaluation of an adaptive learning technology in a first-year extended curriculum programme physics course. South African Computer Journal, 29(3), pp.1–15. URL https://doi.org/10.18489/sacj.v29i3.476. [15] Bayounes, W., Saadi, I.B., Kinshuk and Gh ˆ ezala Ben, H., 2012. Towards a frame- ´ work definition for learning process engineering supported by an adaptive learning system. Proceedings - 2012 IEEE International Conference on Technology Enhanced Education, ICTEE 2012. p.6208662. URL https://doi.org/10.1109/ICTEE.2012. 6208662. [16] Bian, C., Dong, S., Li, C., Shi, Z. and Lu, W., 2017. Generation of adaptive learning path based on concept map and immune algorithm. ICCSE 2017 - 12th International Conference on Computer Science and Education. Institute of Electrical and Electronics Engineers Inc., pp.409–414. URL https://doi.org/10.1109/ICCSE.2017.8085526. [17] Bobrova, T.A., 2011. Theoretical and methodical means of adaptive management socio-pedagogical subsystems of a higher educational institution. (0211U008967). Kharkiv National University of Economics. URL https://nrat.ukrintei.ua/ searchdoc/0211U008967. [18] Borsuk, S.P., 2011. Operators adaptive learning on the functional trainer. The thesis for maintaining the academic degree of candidate of engineering science on speciality 05.07.14 – Air-space trainers. National aviation university, Kyiv. URL https://nrat.ukrintei.ua/searchdoc/0411U007122. [19] Bouzenada, S.N.E., Boissier, O. and Zarour, N.E., 2018. An agent-based approach for personalised and adaptive learning. International Journal of Technology Enhanced Learning, 10(3), pp.184–201. URL https://doi.org/10.1504/IJTEL.2018. 092701. [20] Cabinet of Ministers of Ukraine, 2022. Stratehiia rozvytku vyshchoi osvity v Ukraini na 2022-2032 roky [On approval of the Strategy of higher education development in Ukraine for 2022–2032]. URL https://zakon.rada.gov.ua/laws/show/ 286-2022-%D1%80#Text. [21] Centre for Science and Technology Studies, Leiden University, The Netherlands, 2022. Vosviewer - visualizing scientific landscapes. URL https://www.vosviewer. com/. [22] Chang, Y.H., Chen, Y.Y., Chen, N.S., Lu, Y.T. and Fang, R.J., 2016. Yet another adaptive learning management system based on Felder and Silverman’S Learning Styles and Mashup. Eurasia Journal of Mathematics, Science and Technology Education, 12(5), pp.1273–1285. URL https://doi.org/10.12973/eurasia.2016.1512a. [23] Chaoui, M. and Laskri, M.T., 2013. Proposition and organization of an adaptive learning domain based on fusion from the web. Educational Technology and Society, 16(1), pp.118–132. URL https://api.semanticscholar.org/CorpusID:241317. [24] Chaplot, D.S., Rhim, E. and Kim, J., 2016. Personalized adaptive learning using neural networks. L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale. Association for Computing Machinery, Inc, pp.165–168. URL https://doi.org/10.1145/2876034.2893397. [25] Chauhan, J., Taneja, S. and Goel, A., 2016. Enhancing MOOC with Augmented Reality, Adaptive Learning and Gamification. Proceedings of the 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education, MITE 2015. Institute of Electrical and Electronics Engineers Inc., pp.348–353. URL https: //doi.org/10.1109/MITE.2015.7375343. [26] Cho, K., Raiko, T. and Ilin, A., 2011. Enhanced gradient and adaptive learning rate for training restricted Boltzmann machines. Proceedings of the 28th International Conference on Machine Learning, ICML 2011. pp.105–112. URL https: //www.academia.edu/14198472. [27] Chou, C.Y., Lai, K., Chao, P.Y., Lan, C.H. and Chen, T.H., 2015. Negotiation based adaptive learning sequences: Combining adaptivity and adaptability. Computers and Education, 88, pp.215–226. URL https://doi.org/10.1016/j.compedu.2015. 05.007. [28] Chou, C.Y., Lai, K.R., Chao, P.Y., Tseng, S.F. and Liao, T.Y., 2018. A negotiationbased adaptive learning system for regulating help-seeking behaviors. Computers and Education, 126, pp.115–128. URL https://doi.org/10.1016/j.compedu.2018.07. 010. [29] Chuvasov, M., 2020. Forming the readiness of future teachers to use information and cognitive technologies as a factor of their professionalism and skills development. Bulletin of the Cherkasy Bohdan Khmelnytsky National University. Series “Pedagogical Sciences”, (3), pp.164–168. URL https://ped-ejournal.cdu.edu.ua/ article/view/3939. [30] Clougherty, R.J. and Popova, V., 2015. How adaptive is adaptive learning: Seven models of adaptivity or finding Cinderella’s shoe size. International Journal of Assessment and Evaluation, 22(2), pp.13–22. URL https://doi.org/10.18848/2327-7920/ CGP/v22i02/48369. [31] Cohen, J., 1988. 2nd ed. New York: Routledge. URL https://doi.org/10.4324/ 9780203771587. [32] Covington, M.V., 2014. Patterns of Adaptive Learning Study: Where Do We Go From Here? In: C. Midgley, ed. Goals, Goal Structures, and Patterns of Adaptive Learning. Taylor and Francis, pp.279–294. URL https://doi.org/10.4324/ 9781410602152-17. [33] Cui, W., Xue, Z., Shen, J., Sun, G. and Li, J., 2019. The Item Response Theory Model for an AI-based Adaptive Learning System. 2019 18th International Conference on Information Technology Based Higher Education and Training, ITHET 2019. Institute of Electrical and Electronics Engineers Inc., p.8937383. URL https://doi.org/10. 1109/ITHET46829.2019.8937383. [34] Daniel, J., Cano, E.V. and Gisbert Cervera, M., 2015. The future of MOOCs: Adaptive learning or business model? [El futuro de los MOOC: ¿Aprendizaje adaptado o modelo de negocio?]. RUSC Universities and Knowledge Society Journal, 12(1), pp.64–73. URL https://doi.org/10.7238/rusc.v12i1.2475. [35] Daniello, G., Granito, A., Mangione, G., Miranda, S., Orciuoli, F., Ritrovato, P. and Rossi, P.G., 2014. A city-scale situation-aware adaptive learning system. In: D.G. Sampson, M.J. Spector, N.S. Chen, R. Huang and Kinshuk, eds. Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014. Institute of Electrical and Electronics Engineers Inc., pp.136–137. URL https: //doi.org/10.1109/ICALT.2014.47. [36] Davis, P., Long, P., Adams, D., Corliss, S., Liu, M., McKelroy, E., Tothero, K., Walker, J. and Ziai, K., 2016. Use of adaptive learning to prepare first-year pharmacy students: Our experience. In: J.M. Spector, C.C. Tsai, R. Huang, P. Resta, D.G. Sampson, Kinshuk and N.S. Chen, eds. Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016. Institute of Electrical and Electronics Engineers Inc., pp.72–74. URL https://doi.org/10.1109/ICALT.2016.139. [37] De-La-Fuente-Valent´ın, L., Pardo, A. and Kloos, C.D., 2011. Generic service integration in adaptive learning experiences using IMS learning design. Computers and Education, 57(1), pp.1160–1170. URL https://doi.org/10.1016/j.compedu.2010. 12.007. [38] De Marsico, M., Sterbini, A. and Temperini, M., 2011. The definition of a tunneling strategy between adaptive learning and reputation-based group activities. Proceedings of the 2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT 2011. pp.498–500. URL https://doi.org/10.1109/ICALT.2011.155. [39] De Santana, S.J., Paiva, R., Bittencourt, I.I., Ospina, P.E., De Amorim Silva, R. and Isotani, S., 2016. Evaluating the impact of mars and venus effect on the use of an adaptive learning technology for Portuguese and mathematics. In: J.M. Spector, C.C. Tsai, R. Huang, P. Resta, D.G. Sampson, Kinshuk and N.S. Chen, eds. Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016. Institute of Electrical and Electronics Engineers Inc., pp.31–35. URL https://doi.org/10.1109/ICALT.2016.58. [40] Del Blanco, A., Torrente, J., Moreno-Ger, P. and Fernandez-Manj ´ on, B., 2013. En- ´ hancing adaptive learning and assessment in virtual learning environments with educational games. In: Information Resources Management Association, ed. K12 Education: Concepts, Methodologies, Tools, and Applications. Hershey, PA: IGI Global, vol. 2, pp.578–597. URL https://doi.org/10.4018/978-1-4666-4502-8.ch034. [41] Di Mascio, T., Vittorini, P., Gennari, R., Melonio, A., De La Prieta, F. and Alrifai, M., 2012. The learners’ user classes in the TERENCE adaptive learning system. Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012. pp.572–576. URL https://doi.org/10.1109/ICALT.2012.68. [42] Digital Education Action Plan (2021–2027), 2020. URL https://education.ec.europa. eu/focus-topics/digital-education/digital-education-action-plan. [43] Dong, C. and Sharma, N., 2015. Flipping the classroom with adaptive learning technology. Medical Teacher, 37(10), p.976. URL https://doi.org/10.3109/0142159X. 2015.1045846. [44] Durieu, J., Solal, P. and Tercieux, O., 2011. Adaptive learning and p-best response sets. International Journal of Game Theory, 40(4), pp.735–747. URL https://doi.org/ 10.1007/s00182-010-0266-2. [45] Dziuban, C., Moskal, P., Parker, L., Campbell, M., Howlin, C. and Johnson, C., 2018. Adaptive learning: A stabilizing influence across disciplines and universities. Online Learning Journal, 22(3), pp.7–39. URL https://doi.org/10.24059/olj.v22i3. 1465. [46] Dziuban, C.D., Moskal, P.D., Cassisi, J. and Fawcett, A., 2016. Adaptive learning in psychology: Wayfinding in the digital age. Online Learning Journal, 20(3), pp.74– 96. URL https://doi.org/10.24059/olj.v20i3.972. [47] Elbrekht, O.M., 2004. Adaptable Management of Educational Process in Secondary Comprehensive School with Humanitarian Slant. Thesis for Candidate’s Degree in Pedagogics by speciality 13.00.01 - Theory and History of Pedagogics. Central Institute of Postgraduate Educational Studies of APS of Ukraine, Kyiv. URL https: //nrat.ukrintei.ua/searchdoc/0404U001835. [48] Essa, A., 2016. A possible future for next generation adaptive learning systems. Smart Learning Environments, 3(1), p.16. URL https://doi.org/10.1186/ s40561-016-0038-y. [49] 2014. Evaluating adaptive learning model. Proceedings of 2014 International Conference on Interactive Collaborative Learning, ICL 2014. Institute of Electrical and Electronics Engineers Inc., pp.818–822. URL https://doi.org/10.1109/ICL.2014.7017878. [50] Extavour, R.M., Ocho, O. and Allison, G.L., 2019. Nursing students’ attitudes toward an adaptive learning technology in a pharmacology course. Ubiquitous Learning, 12(4), pp.25–35. URL https://doi.org/10.18848/1835-9795/CGP/v12i04/ 25-35. [51] Fadieieva, L. and Semerikov, S., 2024. ADANCO output on dataset from https://zenodo.org/doi/10.5281/zenodo.10938018. URL https://ssemerikov.github. io/Fadieieva/. [52] Fadieieva, L. and Semerikov, S., 2024. KSPU Moodle activities and marks 2020- 2022. URL https://doi.org/10.5281/zenodo.10938019. [53] Fadieieva, L.O., 2021. Enhancing adaptive learning with Moodle’s machine learning. Educational Dimension, 5, p.1–7. URL https://doi.org/10.31812/ed.625. [54] Fadieieva, L.O., 2023. Adaptive learning: a cluster-based literature review (2011- 2022). Educational Technology Quarterly, 2023(3), p.319–366. URL https://doi.org/ 10.55056/etq.613. [55] Fadieieva, L.O., 2023. Adaptive learning concept selection: a bibliometric review of scholarly literature from 2011 to 2019. Educational Dimension, 9, p.136–148. URL https://doi.org/10.31812/ed.643. [56] Fadieieva, L.O., 2023. Bibliometric Analysis of Adaptive Learning Literature from 2011-2019: Identifying Primary Concepts and Keyword Clusters. In: G. Antoniou, V. Ermolayev, V. Kobets, V. Liubchenko, H.C. Mayr, A. Spivakovsky, V. Yakovyna and G. Zholtkevych, eds. Information and Communication Technologies in Education, Research, and Industrial Applications. Cham: Springer Nature Switzerland, Communications in Computer and Information Science, pp.215–226. URL https://doi.org/10.1007/978-3-031-48325-7_16. [57] Fasihuddin, H., 2016. Enhancing open learning environments (OLEs) using adaptive technologies and learning theories. PhD. University of Newcastle. URL http://hdl. handle.net/1959.13/1321306. [58] Fedoruk, P.I., 2009. Adaptive system of distance learning and knowledge control on the basis of intelligent Internet technologies. Thesis for a Doctor’s of Technical Science degree in speciality 05.13.06 – information technologies. Institute of mathematical machines and systems problems of NAS of Ukraine, Kyiv. URL https://nrat.ukrintei.ua/searchdoc/0510U000090. [59] Fidalgo-Blanco, A., Garcia-Penalvo, F.J., Sein-Echaluce, M.L. and Conde-Gonzalez, M.A., 2014. Learning content management systems for the definition of adaptive learning environments. In: J.L. Sierra-Rodriguez, J.M. Dodero-Beardo and D. Burgos, eds. 2014 International Symposium on Computers in Education, SIIE 2014. Institute of Electrical and Electronics Engineers Inc., pp.105–110. URL https://doi.org/10.1109/SIIE.2014.7017713. [60] Fielder, P.J., 1995. A Comparison of the Effectiveness of Computer Adaptive Testing and Computer Administered Testing. PhD thesis. University of North Texas. URL https://digital.library.unt.edu/ark:/67531/metadc279192/. [61] Fiqri, M. and Nurjanah, D., 2017. Graph-based domain model for adaptive learning path recommendation. IEEE Global Engineering Education Conference, EDUCON. IEEE Computer Society, pp.375–380. URL https://doi.org/10.1109/EDUCON.2017. 7942875. [62] Gasparinatou, A. and Grigoriadou, M., 2011. ALMA: An Adaptive Learning Models environment from texts and Activities that improves students’ science comprehension. Procedia - Social and Behavioral Sciences. vol. 15, pp.2742–2747. URL https://doi.org/10.1016/j.sbspro.2011.04.181. [63] Gavrilovic, N., Arsi ´ c, A., Domazet, D. and Mishra, A., 2018. Algorithm for adaptive ´ learning process and improving learners’ skills in Java programming language. Computer Applications in Engineering Education, 26(5), pp.1362–1382. URL https://doi.org/10.1002/cae.22043. [64] Ghaban, W.H., 2020. Adapting gamification elements to learners’ personality dimensions. Ph.D. thesis. University of Birmingham. URL http://etheses.bham.ac. uk//id/eprint/11053/7/Ghaban2020PhD.pdf. [65] Graf, S., Lin, F., Kinshuk and McGreal, R., 2011. Intelligent and adaptive learning systems: Technology enhanced support for learners and teachers. Hershey, PA: IGI Global. URL https://doi.org/10.4018/978-1-60960-842-2. [66] Grother, T.W., 2019. Response to ‘crowdsourcing for assessment items to support adaptive learning’. Medical Teacher, 41(7), pp.848–849. URL https://doi.org/10. 1080/0142159X.2018.1544419. [67] Hardy, I., Hertel, S., Kunter, M., Klieme, E., Warwas, J., Buttner, G. and Luhken, ¨ A., 2011. Adaptive learning opportunities in elementary school: Characteristics, methodological-didactic emphases, and required teacher competences [Adaptive Lerngelegenheiten in der Grundschule: Merkmale, methodisch-didaktische Schwerpunktsetzungen und erforderliche Lehrerkompetenzen]. Zeitschrift fur Padagogik, 57(6), pp.819–833. URL https://doi.org/10.25656/01:8783. [68] Hardy, M., Wiebe, E.N., Grafsgaard, J.F., Boyer, K.E. and Lester, J.C., 2013. Physiological Responses to Events during Training: Use of Skin Conductance to Inform Future Adaptive Learning Systems. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), pp.2101–2105. URL https://doi.org/10. 1177/1541931213571468. [69] Heffernan, N.T., Ostrow, K.S., Kelly, K., Selent, D., Van Inwegen, E.G., Xiong, X. and Williams, J.J., 2016. The Future of Adaptive Learning: Does the Crowd Hold the Key? International Journal of Artificial Intelligence in Education, 26(2), pp.615– 644. URL https://doi.org/10.1007/s40593-016-0094-z. [70] Heiyanthuduwage, S.R., Schwitter, R. and Orgun, M.A., 2013. An adaptive learning system using plug and play ontologies. Proceedings of 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2013. pp.429– 434. URL https://doi.org/10.1109/TALE.2013.6654476. [71] Henseler, J., 2017. ADANCO 2.0.1 User Manual. URL https://ris.utwente.nl/ws/ portalfiles/portal/5135104/ADANCO_2-0-1.pdf. [72] Henseler, J., 2021. Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables, Methodology in the Social Sciences. New York, London: The Guilford Press. [73] Hertel, S., Warwas, J. and Klieme, E., 2011. Individual fostering and adaptive learning opportunities in elementary school instruction. An introduction [Individuelle Forderung und adaptive Lerngelegenheiten im Grundschulunterricht. Ein- ¨ leitung in den Thementeil]. Zeitschrift fur Padagogik, 57(6), pp.803–804. URL https://www.fachportal-paedagogik.de/literatur/vollanzeige.html?FId=3146197. [74] Hou, M. and Fidopiastis, C., 2017. A generic framework of intelligent adaptive learning systems: from learning effectiveness to training transfer. Theoretical Issues in Ergonomics Science, 18(2), pp.167–183. URL https://doi.org/10.1080/ 1463922X.2016.1166405. [75] How, M.L. and Hung, W.L.D., 2019. Educational Stakeholders’ independent evaluation of an artificial intelligence-enabled adaptive learning system using bayesian network predictive simulations. Education Sciences, 9(2), p.110. URL https://doi.org/10.3390/educsci9020110. [76] Hu, Q. and Huang, Y., 2014. An approach for designing test bank in adaptive learning system. Proceedings of the 9th International Conference on Computer Science and Education, ICCCSE 2014. Institute of Electrical and Electronics Engineers Inc., pp.462–464. URL https://doi.org/10.1109/ICCSE.2014.6926504. [77] Huang, L.H., Dow, C.R., Li, Y.H. and Hsuan, P., 2013. U-TA: A ubiquitous teaching assistant using knowledge retrieval and adaptive learning techniques. Computer Applications in Engineering Education, 21(2), pp.245–255. URL https://doi.org/10. 1002/cae.20466. [78] Huang, S.L. and Shiu, J.H., 2012. A user-centric adaptive learning system for elearning 2.0. Educational Technology and Society, 15(3), pp.214–225. URL https: //www.researchgate.net/publication/285724853. [79] Huang, X., Yang, K. and Lawrence, V., 2015. Classification-based approach to concept map generation in adaptive learning. In: N.S. Chen, T.C. Liu, Kinshuk, R. Huang, G.J. Hwang, D.G. Sampson and C.C. Tsai, eds. Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015. Institute of Electrical and Electronics Engineers Inc., pp.19–23. URL https: //doi.org/10.1109/ICALT.2015.149. [80] Huffman, M., Gustafson, S., Chatterjee, S., Rabner, M., Nundy, S., Gerkovich, M.M. and Wright, S.M., 2018. Bolstering diagnostic reasoning skills with adaptive learning. Medical Teacher, 40(8), pp.845–849. URL https://doi.org/10.1080/0142159x. 2018.1484561. [81] Hwang, G.J., Sung, H.Y., Hung, C.M. and Huang, I., 2013. A learning style perspective to investigate the necessity of developing adaptive learning systems. Educational Technology and Society, 16(2), pp.188–197. URL https://www.researchgate. net/publication/279711154. [82] Ham¨ al¨ ainen, W., Kumpulainen, V. and Mozgovoy, M., 2014. Evaluation of cluster- ¨ ing methods for adaptive learning systems. In: U. Kose and D. Koc, eds. Artificial Intelligence Applications in Distance Education. Hershey, PA: IGI Global, pp.237– 260. URL https://doi.org/10.4018/978-1-4666-6276-6.ch014. [83] Ibrahim, M.S. and Hamada, M., 2016. Adaptive learning framework. 2016 15th International Conference on Information Technology Based Higher Education and Training, ITHET 2016. Institute of Electrical and Electronics Engineers Inc., p.7760738. URL https://doi.org/10.1109/ITHET.2016.7760738. [84] Idrobo, C.J. and Davidson-Hunt, I.J., 2012. Adaptive learning, technological innovation and livelihood diversification: The adoption of pound nets in Rio de Janeiro state, Brazil. Maritime Studies, 11(1), pp.1–22. URL https://doi.org/10. 1186/2212-9790-11-3. [85] Indrayadi, F. and Nurjanah, D., 2015. Combining learner’s preference and similar peers’ experience in adaptive learning. In: M. Helfert, M.T. Restivo, S. Zvacek and J. Uhomoibhi, eds. CSEDU 2015 - 7th International Conference on Computer Supported Education, Proceedings. SciTePress, vol. 1, pp.486–493. URL https://doi. org/10.5220/0005490904860493. [86] Isaksson, E., Naeve, A. and Lefrere, P., 2016. Performance Augmentation Through ` Ubiquitous and Adaptive Learning and Work Environments. In: Y. Li, M. Chang, M. Kravcik, E. Popescu, R. Huang, Kinshuk and N.S. Chen, eds. State-of-the-Art and Future Directions of Smart Learning. Singapore: Springer Singapore, pp.315–319. URL https://doi.org/10.1007/978-981-287-868-7_39. [87] Iyer, S.S., Gernal, L., Subramanian, R. and Mehrotra, A., 2023. Impact of digital disruption influencing business continuity in UAE higher education. Educational Technology Quarterly, 2023(1), p.18–57. URL https://doi.org/10.55056/etq.29. [88] Jiang, S., 2020. On-The-Fly Parameter Estimation Based on Item Response Theory in Item-based Adaptive Learning Systems. PhD, Psychology. University of Minnesota. URL http://hdl.handle.net/11299/218710. [89] Johnson, S. and Zaiane, O.R., 2017. Learning to Analyze Medical Images: A Smart Adaptive Learning Environment for an Ill-Defined Domain. In: E. Popescu, Kinshuk, M.K. Khribi, R. Huang, M. Jemni, N.S. Chen and D.G. Sampson, eds. Innovations in Smart Learning. Singapore: Springer Singapore, pp.103–112. URL https://doi.org/10.1007/978-981-10-2419-1_15. [90] Johnson, Z., 2016. Teachers as designers of context-adaptive learning experience. In: S. Goldman and Z. Kabayadondo, eds. Taking Design Thinking to School: How the Technology of Design Can Transform Teachers, Learners, and Classrooms. Taylor and Francis, pp.126–142. URL https://doi.org/10.4324/9781317327585. [91] Jonsdottir, A.H., Jakobsdottir, A. and Stefansson, G., 2015. Development and use of an adaptive learning environment to research online study behaviour. Educational Technology and Society, 18(1), pp.132–144. URL https://www.researchgate. net/publication/282378459. [92] Jordan, C., 2013. Comparison of International Baccalaureate (IB) chemistry students’ preferred vs actual experience with a constructivist style of learning in a Moodle e-learning environment. International Journal for Lesson and Learning Studies, 2(2), p.155–167. URL https://doi.org/10.1108/20468251311323397. [93] Kabudi, T., Pappas, I. and Olsen, D.H., 2021. AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, p.100017. URL https://doi.org/10.1016/j.caeai.2021.100017. [94] Kaensar, C. and Wongnin, W., 2023. Analysis and Prediction of Student Performance Based on Moodle Log Data using Machine Learning Techniques. International Journal of Emerging Technologies in Learning. vol. 18, pp.184 – 203. URL https://doi.org/10.3991/ijet.v18i10.35841. [95] Kakish, K. and Pollacia, L., 2018. Adaptive learning to improve student success and instructor efficiency in introductory computing course. In: B.L. Garner, ed. Proceedings of the 34th Information Systems Education Conference, ISECON 2018. Foundation for Information Technology Education, pp.72–78. URL https://www. researchgate.net/publication/324574230. [96] Kakish, K., Robertson, C. and Jonassen, L., 2019. Understanding perceptions of conceptual information technology adaptive learning. In: B.L. Garner, ed. Proceedings of the Information Systems Education Conference, ISECON. Foundation for Information Technology Education, vol. 2019-April, pp.47–54. URL http: //proceedings.isecon.org/download/howeebs8i9p1d9jophtt. [97] Kakosimos, K.E., 2015. Example of a micro-adaptive instruction methodology for the improvement of flipped-classrooms and adaptive-learning based on advanced blended-learning tools. Education for Chemical Engineers, 12, pp.1–11. URL https: //doi.org/10.1016/j.ece.2015.06.001. [98] Kardan, A., Imani, M.B. and Ebrahim, M.A., 2013. A novel adaptive learning path method. 4th International Conference on e-Learning and e-Teaching, ICELET 2013. pp.20–25. URL https://doi.org/10.1109/ICELET.2013.6681639. [99] Kaw, A., Clark, R., Delgado, E. and Abate, N., 2019. Analyzing the use of adaptive learning in a flipped classroom for preclass learning. Computer Applications in Engineering Education, 27(3), pp.663–678. URL https://doi.org/10.1002/cae.22106. [100] Kellman, P.J. and Krasne, S., 2018. Accelerating expertise: Perceptual and adaptive learning technology in medical learning. Medical Teacher, 40(8), pp.797–802. URL https://doi.org/10.1080/0142159X.2018.1484897. [101] Kerr, P., 2016. Adaptive learning. ELT Journal, 70(1), pp.88–93. URL https://doi. org/10.1093/elt/ccv055. [102] Krahn, T., Kuo, R. and Chang, M., 2023. Personalized Study Guide: A Moodle Plugin Generating Personal Learning Path for Students. In: C. Frasson, P. Mylonas and C. Troussas, eds. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, vol. 13891 LNCS, pp.333 – 341. URL https://doi.org/10.1007/978-3-031-32883-1_30. [103] Kuzikov, B.O., 2014. Information technology of analysis and synthesis of adaptive e-learning system. The dissertation on competition of a scientific degree of a Cand.Tech.Sci on a specialty 05.13.06 – information technology. National Technical University “Kharkiv Polytechnic Institute”’, Kharkiv. URL https://nrat.ukrintei. ua/searchdoc/0414U003734. [104] Kyrychenko, I.V., 2019. Information technology of content identification in adaptive e-learning systems. Dissertation for the degree of a candidate of technical sciences in specialty 05.13.06 – «Information technologies». Kremenchuk Mykhailo Ostrohradskyi National University, Kremenchuk. URL https://nrat.ukrintei.ua/ searchdoc/0419U002452. [105] Lai, K.R., Chou, C.Y. and Lan, C.H., 2012. Supporting adaptive learning sequences with agent negotiation. Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012. pp.506–508. URL https://doi.org/10. 1109/ICALT.2012.26. [106] Laitinen, I., Piazza, R. and Stenvall, J., 2017. Adaptive learning in smart cities – The cases of Catania and Helsinki. Journal of Adult and Continuing Education, 23(1), pp.119–137. URL https://doi.org/10.1177/1477971417691781. [107] Lestari, W., Nurjanah, D. and Selviandro, N., 2017. Adaptive presentation based on learning style and working memory capacity in adaptive learning system. In: P. Escudeiro, S. Zvacek, B.M. McLaren, J. Uhomoibhi and G. Costagliola, eds. CSEDU 2017 - Proceedings of the 9th International Conference on Computer Supported Education. SciTePress, vol. 1, pp.363–370. URL https://www.researchgate. net/publication/320699853. [108] Levy, J.C., 2015. Adaptive Learning and the Human Condition. Taylor and Francis. URL https://doi.org/10.4324/9781315665160. [109] Li, H., Cui, W., Xu, Z., Zhu, Z. and Feng, M., 2018. Yixue adaptive learning system and its promise on improving student learning. In: B.M. McLaren, R. Reilly, S. Zvacek and J. Uhomoibhi, eds. CSEDU 2018 - Proceedings of the 10th International Conference on Computer Supported Education. SciTePress, vol. 2, pp.45–52. URL https://doi.org/10.5220/0006689800450052. [110] Li, R., 2019. Adaptive learning model based on ant colony algorithm. International Journal of Emerging Technologies in Learning, 14(1), pp.49–57. URL https://doi.org/ 10.3991/ijet.v14i01.9487. [111] Li, Y.H., Zhao, B. and Gan, J.H., 2015. Make adaptive learning of the MOOC: The CML model. 10th International Conference on Computer Science and Education, ICCSE 2015. Institute of Electrical and Electronics Engineers Inc., pp.1001–1004. URL https://doi.org/10.1109/ICCSE.2015.7250398. [112] Li, Z., Yang, Y., Wu, Y. and Jia, J., 2013. An adaptive Learning system based on the case. Proceedings of the 8th International Conference on Computer Science and Education, ICCSE 2013. pp.1431–1435. URL https://doi.org/10.1109/ICCSE.2013. 6554150. [113] Lin, C.C. and Wu, Y.T., 2018. The effectiveness of integrating adaptive learning platform with flipped classroom in students’ learning performance and selflearning approach. In: L.H. Wong, M. Banawan, N. Srisawasdi, J.C. Yang, M.M.T. Rodrigo, M. Chang and Y.T. Wu, eds. ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings. Asia-Pacific Society for Computers in Education, pp.304–308. URL https://scholars.ncu.edu.tw/en/publications/ the-effectiveness-of-integrating-adaptive-learning-platform-with-. [114] Lin, C.C., Wu, Y.T. and Cheng, T.Y., 2017. Online knowledge-structurebased adaptive science learning: Integrates adaptive dynamic assessment into adaptive learning. In: T. Hayashi Y. amd Supnithi, M. Mathews, S.L. Wong, A.F. Mohd Ayub, A. Mitrovic, W. Chen and J.C. Yang, eds. ICCE 2017 - 25th International Conference on Computers in Education: Technology and Innovation: Computer-Based Educational Systems for the 21st Century, Workshop Proceedings. Asia-Pacific Society for Computers in Education, pp.595–600. URL https://scholars.ncu.edu.tw/en/publications/ online-knowledge-structure-based-adaptive-science-learning-integr. [115] Linden, K., Webster, L., Pemberton, L. and Davison, W.R., 2017. Can we calm first-year student’s “neuroscience anxiety” with adaptive learning resources? A pilot study. In: S. Krishnan, S. Macfarlane, L. Ngo, J. Tai, D. Blake, I. Doherty, I. Story, M. Campbell, J. Willems, C. Adachi, S. Palmer, M. O’Donnell, L. Riddell and H. Suri, eds. ASCILITE 2018 - Conference Proceedings - 35th International Conference of Innovation, Practice and Research in the use of Educational Technologies in Tertiary Education: Open Oceans: Learning Without Borders. Australasian Society for Computers in Learning in Tertiary Education (ASCILITE), pp.451–455. URL https://researchoutput.csu.edu.au/en/publications/ can-we-calm-first-year-students-neuroscience-anxiety-with-adaptiv. [116] Lishon-Savarino, N.A., 2017. A Conspectus review of adaptive learning technology’s instructional design techniques for developmental mathematics using Gagne’s nine events of instruction. In: P. Roubides, ed. ´ Distance Learning: Perspectives, Outcomes and Challenges. Nova Science Publishers, Inc., Education in a Competitive and Globalizing World, pp.147–164. [117] Liu, M., Kang, J., Zou, W., Lee, H., Pan, Z. and Corliss, S., 2017. Using Data to Understand How to Better Design Adaptive Learning. Technology, Knowledge and Learning, 22(3), pp.271–298. URL https://doi.org/10.1007/s10758-017-9326-z. [118] Liu, M., McKelroy, E., Corliss, S. and Carrigan, J., 2017. Investigating the effect of an adaptive learning intervention on students’ learning. Educational Technology Research and Development, 65(6), pp.1605–1625. URL https://doi.org/10.1007/ s11423-017-9542-1. [119] Martinez, D.V., Shafik, R.A., Acharyya, A. and Merrett, G., 2016. Design and implementation of an adaptive learning system: An MSc project experience. 2016 11th European Workshop on Microelectronics Education, EWME 2016. Institute of Electrical and Electronics Engineers Inc., p.7496481. URL https://doi.org/10.1109/ EWME.2016.7496481. [120] Martynova, Y.V., 2011. Informational technologies of adaptive computerized system for teaching and testing. Dissertation for proving the master degree of scientific branch 05.13.06 – information technologies. Kherson National Technical University, Kherson. URL https://nrat.ukrintei.ua/searchdoc/0412U001155. [121] Massey, C.M., Kellman, P.J., Roth, Z. and Burke, T., 2013. Perceptual learning and adaptive learning technology: Developing new approaches to mathematics learning in the classroom. Taylor and Francis. URL https://doi.org/10.1007/ 978-3-030-95633-2_5. [122] Matar, N., 2011. Adaptive Learning Objects Repository structure towards unified e-learning. International Conference on Information Society, i-Society 2011. IEEE Computer Society, pp.404–410. URL https://doi.org/10.1109/i-society18435.2011. 5978480. [123] Matthews, K., Janicki, T., He, L. and Patterson, L., 2012. Implementation of an automated grading system with an adaptive learning component to affect student feedback and response time. Journal of Information Systems Education, 23(1), pp.71–80. URL https://aisel.aisnet.org/jise/vol23/iss1/7. [124] Mavroudi, A., Giannakos, M. and Krogstie, J., 2016. Insights on the interplay between adaptive learning and learning analytics. In: J.M. Spector, C.C. Tsai, R. Huang, P. Resta, D.G. Sampson, Kinshuk and N.S. Chen, eds. Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016. Institute of Electrical and Electronics Engineers Inc., pp.42–43. URL https: //doi.org/10.1109/ICALT.2016.84. [125] Mavroudi, A., Giannakos, M. and Krogstie, J., 2018. Supporting adaptive learning pathways through the use of learning analytics: developments, challenges and future opportunities. Interactive Learning Environments, 26(2), pp.206–220. URL https://doi.org/10.1080/10494820.2017.1292531. [126] Mavroudi, A., Hadzilacos, T., Kalles, D. and Gregoriades, A., 2016. Teacher-led design of an adaptive learning environment. Interactive Learning Environments, 24(8), pp.1996–2010. URL https://doi.org/10.1080/10494820.2015.1073747. [127] Mazurets, O.V., 2020. Information Technology for Automated Structuring of Educational Materials and Creation of Tests for Adaptive Control of the Level of Knowledge. The thesis for the degree of candidate of technical sciences, specialty 05.13.06 «Information technology». Ternopil National Economic University, Ternopil. URL https://nrat.ukrintei.ua/searchdoc/0420U101209. [128] Mccaslin, M. and Burross, H.L., 2011. Research on individual differences within a sociocultural perspective: Co-regulation and adaptive learning. Teachers College Record, 113(2), pp.325–349. URL https://doi.org/10.1177/016146811111300203. [129] Mehigan, T.J., 2013. Automatic detection of learner-style for adaptive eLearning. PhD thesis. University College Cork. URL http://hdl.handle.net/10468/1334. [130] Mei, J., Guo, Y. and Li, X., 2017. Adaptive Learning Mode of a Multimedia-based “English Literature” Learning System . International Journal of Emerging Technologies in Learning, 12(1), pp.71–83. URL https://doi.org/10.3991/ijet.v12i01.6483. [131] Menon, A., Gaglani, S., Haynes, M. and Tackett, S., 2017. Using “big data” to guide implementation of a web and mobile adaptive learning platform for medical students. Medical Teacher, 39(9), pp.975–980. URL https://doi.org/10.1080/0142159X. 2017.1324949. [132] Midgley, C., 2014. Goals, Goal Structures, and Patterns of Adaptive Learning. Taylor and Francis. URL https://doi.org/10.4324/9781410602152. [133] Miller, L.A., Asarta, C.J. and Schmidt, J.R., 2019. Completion deadlines, adaptive learning assignments, and student performance. Journal of Education for Business, 94(3), pp.185–194. URL https://doi.org/10.1080/08832323.2018.1507988. [134] Ministry of Education and Science of Ukraine, 2018. Nakaz pro zatverdzhennia Kontseptsii rozvytku pedahohichnoi osvity [Order on approval of the Concept of pedagogical education development]. URL https://mon.gov.ua/ua/npa/ pro-zatverdzhennya-koncepciyi-rozvitku-pedagogichnoyi-osviti. [135] Minkovska, D., Ivanova, M. and Yordanova, M., 2016. Didactic principles of eLearning -Design and implementation of an interactive adaptive learning system. 2016 15th International Conference on Information Technology Based Higher Education and Training, ITHET 2016. Institute of Electrical and Electronics Engineers Inc., p.7760709. URL https://doi.org/10.1109/ITHET.2016.7760709. [136] Mirata, V. and Bergamin, P., 2019. Developing an implementation framework for adaptive learning: A case study approach. In: R. Orngreen, M. Buhl and B. Meyer, eds. Proceedings of the European Conference on e-Learning, ECEL. Academic Conferences Limited, vol. 2019-November, pp.668–673. URL https://doi.org/10.34190/ EEL.19.024. [137] Miyagi, T., 2011. An adaptive learning algorithm for a route choice problem in uncertain traffic environments. WIT Transactions on the Built Environment. vol. 116, pp.43–52. URL https://doi.org/10.2495/UT110041. [138] Moreno-Marcos, P.M., Barredo, J., Munoz-Merino, P.J. and Delgado Kloos, C., ˜ 2023. Statoodle: A Learning Analytics Tool to Analyze Moodle Students’ Actions and Prevent Cheating. In: O. Viberg, I. Jivet, P.J. Munoz-Merino, M. Perifanou ˜ and T. Papathoma, eds. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, vol. 14200 LNCS, pp.736 – 741. URL https://doi.org/10.1007/978-3-031-42682-7_70. [139] Nakic, J., Granic, A. and Glavinic, V., 2015. Anatomy of student models in adaptive learning systems: A systematic literature review of individual differences from 2001 to 2013. Journal of Educational Computing Research, 51(4), pp.459–489. URL https://doi.org/10.2190/EC.51.4.e. [140] Nasr-Eddine, B.S., Zaidi, S. and Eddine, Z.N., 2012. An architecture of an adaptive learning system. 2012 International Conference on Education and e-Learning Innovations, ICEELI 2012. p.6360622. URL https://doi.org/10.1109/ICEELI.2012.6360622. [141] Natriello, G., 2017. The adaptive learning landscape. Teachers College Record, 119(3), p.030309. URL https://doi.org/10.1177/01614681171190030. [142] Natriello, G. and Chae, H.S., 2017. Introduction to the special issue on adaptive learning opportunities. Teachers College Record, 119(3), p.030301. URL https://doi. org/10.1177/016146811711900304. [143] Nesterova, M., 2015. Informatsiino-kohnityvni tekhnolohii v systemi vyshchoi osvity suspilstva znan. Vyshcha osvita Ukrainy, (1), pp.40–45. URL http://nbuv. gov.ua/UJRN/vou_2015_1_7. [144] Nesterova, M.O., 2015. Modern Cognitive Researches: Philosophical-methodological Conceptualisation Contexts. Thesis for the degree of Doctor of Sciences in Philosophy, Speciality 09.00.09 – Philosophy of Science. Taras Shevchenko National University of Kyiv, Kyiv. URL https://nrat.ukrintei.ua/searchdoc/0516U000888. [145] Nguyen, L., 2014. A user modeling system for adaptive learning. Proceedings of 2014 International Conference on Interactive Collaborative Learning, ICL 2014. Institute of Electrical and Electronics Engineers Inc., pp.864–866. URL https://doi. org/10.1109/ICL.2014.7017887. [146] Nguyen, V.A., 2012. Toward an adaptive learning system framework: Using bayesian network to manage learner model. International Journal of Emerging Technologies in Learning, 7(4), pp.38–47. URL https://doi.org/10.3991/ijet.v7i4. 2290. [147] Nurjanah, D., 2014. The granularity of collaborative work for creating adaptive learning resources. CSEDU 2014 - Proceedings of the 6th International Conference on Computer Supported Education. SciTePress, vol. 1, pp.165–173. URL https://doi. org/10.5220/0004722701650173. [148] Nurjanah, D., 2016. Good and similar learners’ recommendation in adaptive learning systems. In: J. Uhomoibhi, G. Costagliola, S. Zvacek and B.M. McLaren, eds. CSEDU 2016 - Proceedings of the 8th International Conference on Computer Supported Education. SciTePress, vol. 1, pp.434–440. URL https://doi.org/10.5220/0005864304340440. [149] Nurjanah, D., 2018. LifeOn, a ubiquitous lifelong learner model ontology supporting adaptive learning. IEEE Global Engineering Education Conference, EDUCON. IEEE Computer Society, vol. 2018-April, pp.866–871. URL https://doi.org/10.1109/ EDUCON.2018.8363321. [150] Nussbaumer, A., Steiner, C.M., Hillemann, E.C. and Albert, D., 2014. Towards an evaluation service for adaptive learning systems. In: H. Ogata, L. LomickaAnderson, C.S. Chai, R. Hampel, Y. Hayashi, J. Vassileva, C.C. Liu, W. Chen, J. Hsu, Y.J. Lan, J. Mason, M. Yamada, H.Y. Shyu, A. Weerasinghe, Y.T. Wu, L. Zhang, Kinshuk, Y. Matsubara, Y. Miao, H. Ogata, S.C. Kong, M. Chang, M.S.Y. Jong, R. Kuo, R. Robson, B. Wasson, A. Kashihara, U. Cress, M. Jansen, J. Oshima, C. Yin, J. Zhang and C. Chinn, eds. Proceedings of the 22nd International Conference on Computers in Education, ICCE 2014. Asia-Pacific Society for Computers in Education, pp.138– 140. URL https://www.researchgate.net/publication/289630492. [151] Nye, B.D., Pavlik, P. I., J., Windsor, A., Olney, A.M., Hajeer, M. and Hu, X., 2018. SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics. International Journal of STEM Education, 5(1), p.12. URL https://doi.org/10.1186/ s40594-018-0109-4. [152] Obikwelu, C. and Read, J., 2013. Serious game adaptive learning systems. 7th European Conference on Games Based Learning, ECGBL 2013. vol. 2, pp.442–449. URL https://issuu.com/acpil/docs/ecgbl2013-issuu_vol_2. [153] Oke, I., Kulkarni, P., Lolap, T. and Haribhakta, Y., 2017. Adaptive Learning System for Analysis of Performance and Learning Behavior of School Children. In: V. Kumar, Kinshuk and S. Murthy, eds. Proceedings - IEEE 8th International Conference on Technology for Education, T4E 2016. Institute of Electrical and Electronics Engineers Inc., pp.238–239. URL https://doi.org/10.1109/T4E.2016.058. [154] Oliveira, E.H.T., Nozawa, E.H., Costa, L.F., Vicari, R.M. and Albuquerque, Y.C., 2012. Work in progress: Towards a framework for adaptive learning systems. Proceedings - Frontiers in Education Conference, FIE. p.6462239. URL https://doi. org/10.1109/FIE.2012.6462239. [155] Osadchyi, V.V., 2023. Adaptive system for individualization and personalization of professional training of future specialists in blended learning. (0223U003360). Bogdan Khmelnitsky Melitopol State Pedagogical University. URL https://nrat. ukrintei.ua/searchdoc/0223U003360. [156] Osadchyy, V.V., 2020. Adaptyvna systema dlia indyvidualizatsii ta personalizatsii profesiinoi pidhotovky maibutnikh fakhivtsiv v umovakh zmishanoho navchannia (promizhnyi) [Adaptive system for individualization and personalization of professional training of future specialists in conditions of mixed learning (interim)]. (0221U101936). Melitopol State Pedagogical University. URL https://nrat.ukrintei. ua/searchdoc/0221U101936. [157] Osadchyy, V.V., 2022. Adaptyvna systema dlia indyvidualizatsii ta personalizatsii profesiinoi pidhotovky maibutnikh fakhivtsiv v umovakh zmishanoho navchannia (promizhnyi) [Adaptive system for individualization and personalization of professional training of future specialists in conditions of mixed learning (interim)]. (0222U000480). Melitopol State Pedagogical University. URL https://nrat.ukrintei. ua/searchdoc/0222U000480. [158] Palomino, C.E.G., Silveira, R.A. and Nakayama, M.K., 2015. Using agent-based adaptive learning environments for knowledge sharing management. International Journal of Knowledge and Learning, 10(3), pp.278–295. URL https://doi.org/10.1504/IJKL.2015.073475. [159] Panicker, R.C., Kumar, A., Srinivasan, D. and John, D., 2019. Adaptive Learning and Analytics in Engineering Education. In: M.J.W. Lee, S. Nikolic, G.K.W. Wong, J. Shen, M. Ros, L.C.U. Lei and N. Venkatarayalu, eds. Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018. Institute of Electrical and Electronics Engineers Inc., pp.1193–1196. URL https://doi.org/10.1109/TALE.2018.8615200. [160] Pedan, S.I., 2012. Models and methods of information technology of competenceoriented adaptive tutoring support. Thesis for candidate of technical sciences. degree on the specialty 05.13.06 — information technologies. National Aerospace University “Kharkiv Aviation Institute”, Kharkiv. URL https://nrat.ukrintei.ua/ searchdoc/0412U003745. [161] Pelanek, R., 2018. Exploring the utility of response times and wrong answers for ´ adaptive learning. Proceedings of the Fifth Annual ACM Conference on Learning at Scale. New York, NY, USA: Association for Computing Machinery, L@S ’18, p.18. URL https://doi.org/10.1145/3231644.3231675. [162] Peng, H., Ma, S. and Spector, J.M., 2019. Personalized Adaptive Learning: An Emerging Pedagogical Approach Enabled by a Smart Learning Environment. In: M. Chang, E. Popescu, Kinshuk, N.S. Chen, M. Jemni, R. Huang, J.M. Spector and D.G. Sampson, eds. Foundations and Trends in Smart Learning. Singapore: Springer, Lecture Notes in Educational Technology, pp.171–176. URL https://doi.org/10. 1007/978-981-13-6908-7_24. [163] Perez-Suay, A., Van Vaerenbergh, S., Diago, P.D., Pascual-Venteo, A.B. and Ferri, F.J., 2023. Data-Driven Modeling Through the Moodle Learning Management System: An Empirical Study Based on a Mathematics Teaching Subject. Revista Iberoamericana de Tecnologias del Aprendizaje. Education Society of IEEE (Spanish Chapter), vol. 18, pp.19 – 27. URL https://doi.org/10.1109/RITA.2023.3250434. [164] Philosophy, 2006. URL https://web.archive.org/web/20060901040155/http://docs. moodle.org/en/Philosophy. [165] Pikulyak, M.V., 2016. Methods and tools for forming of adaptive system of distance education. The thesis for the degree of candidate of technical sciences, specialty 05.13.06 – information technologies. Ternopil National Economic University, Ternopil. URL http://dspace.wunu.edu.ua/handle/316497/4531. [166] Pliakos, K., Joo, S.H., Park, J.Y., Cornillie, F., Vens, C. and Noortgate, W. Van den, 2019. Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Computers and Education, 137, pp.91–103. URL https://doi.org/10.1016/j.compedu.2019.04.009. [167] Pogrebniuk, I.M., 2013. Modelling of the adaptive learning scenarios with the use of Petri nets. Dissertation thesis for the degree of candidate of technical sciences, speciality 05.13.06 – information technologies. National Transport University, Kyiv. URL https://nrat.ukrintei.ua/searchdoc/0413U002243. [168] Qu, Y., Cai, R. and Haj-Hussein, M., 2019. Research and Practice of Applying Adaptive Learning in Computer Science and IT Degree Programs. Proceedings - Frontiers in Education Conference, FIE. Institute of Electrical and Electronics Engineers Inc., vol. 2019-October, p.9028374. URL https://doi.org/10.1109/FIE43999.2019.9028374. [169] Quispe-Pari, E., Tamo-Vargas, G., Bedregal-Alpaca, N., Guevara, K., DelgadoBarra, L. and Laura-Ochoa, L., 2023. Adaptability in Moodle, Case: Grading restrictions; [Adaptabilidad en Moodle, Caso: Restricciones en las calificaciones]. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao. Associacao Iberica de Sistemas e Tecnologias de Informacao, vol. 2023, pp.195 – 208. URL https://pure.unsa.edu.pe/en/publications/ adaptabilidad-en-moodle-caso-restricciones-en-las-calificaciones. [170] Regulation on the system of assessment of the students’ learning outcomes in the Kryvyi Rih State Pedagogical University [Polozhennia pro systemu otsiniuvannia navchalnykh dosiahnen studentiv u Kryvorizkomu derzhavnomu pedahohichnomu universyteti], 2022. URL https://drive.google.com/file/d/ 1DIvAyjUfmbqj93eqLJfHcYmNlJ3mx24t/view. [171] Regulation on using the distance learning technologies in the educational activity of the Kryvyi Rih State Pedagogical University [Polozhennia pro vykorystannia tekhnolohii dystantsiinoho navchannia v osvitnii diialnosti Kryvorizkoho derzhavnoho Pedahohichnoho universytetu], 2021. URL https://drive.google.com/ file/d/1VxVOH3vSMeq6vLXdAKZzHonmcKa2jw97/view. [172] Rimbaud, Y., McEwan, T., Lawson, A. and Cairncross, S., 2015. Adaptive learning in computing for non-native speakers. Proceedings - Frontiers in Education Conference, FIE. Institute of Electrical and Electronics Engineers Inc., vol. 2015-February, p.7044142. URL https://doi.org/10.1109/FIE.2014.7044142. [173] Rodrigues Pereira, J.C., Dos Anjos Formiga Cabral, L., Dos Santos Oiveira, R., Bezerra, L.L. and De Melo, N.M.T., 2012. OAEditor - A framework for editing adaptive learning objects. IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2012. pp.236–240. URL https://www.iadisportal. org/digital-library/oaeditor-a-framework-for-editing-adaptive-learning-objects. [174] Rosen, Y., Munson, L., Lopez, G., Rushkin, I., Ang, A., Tingley, D., Rubin, R. and Weber, G., 2018. The effects of adaptive learning in a massive open online course on learners’ skill development. Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018. Association for Computing Machinery, Inc, p.6. URL https://doi.org/10.1145/3231644.3231651. [175] Rudnitska, O., 2020. Information technology for the development of the continuing education adaptive system for smart industries. Dissertation for the Doctor of Philosophy degree of technical sciences in specialty 122 "Computer Science". Kyiv National University of Construction and Architecture of the Ministry of Education and Science of Ukraine, Kyiv. URL https://nrat.ukrintei.ua/searchdoc/ 0820U100340/. [176] Sandman, T.E., 2014. A preliminary investigation into the adaptive learning styles of business students. Decision Sciences Journal of Innovative Education, 12(1), pp.33–54. URL https://doi.org/10.1111/dsji.12020. [177] Schaul, T. and LeCun, Y., 2013. Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients. 1st International Conference on Learning Representations, ICLR 2013 - Conference Track Proceedings. International Conference on Learning Representations, ICLR. URL https://doi.org/10.48550/arXiv. 1301.3764. [178] Sergeev, K., Mironenko, O., Krivich, O.Y., Petrov, A. and Kozlov, M., 2023. Using the module “analytics and machine learning” in LMS moodle at training students of specialty “rolling stock”. In: A.G. Galkin and S.V. Bushuev, eds. AIP Conference Proceedings. American Institute of Physics Inc., vol. 2624. URL https://doi.org/10. 1063/5.0133923. [179] Seriakov, A. and Shcheholkova, V., 2021. Modeli ta metody v adaptyvnykh systemakh navchannia (promizhnyi) [Models and methods in adaptive learning systems (interim)]. (0221U103332). Shostkin Institute of Sumy State University. URL https://nrat.ukrintei.ua/searchdoc/0221U103332. [180] Serrao-Neumann, S., Cox, M. and Low Choy, D., 2019. Bridging Adaptive Learning and Desired Natural Resource Management Outcomes: Insights from Australian Planners. Planning Practice and Research, 34(2), pp.149–167. URL https://doi.org/ 10.1080/02697459.2018.1549188. [181] Sfenrianto, S., Hartarto, Y., Akbar, H., Mukhtar, M., Efriadi, E. and Wahyudi, M., 2018. An adaptive learning system based on knowledge level for English learning. International Journal of Emerging Technologies in Learning, 13(12), pp.191–200. URL https://doi.org/10.3991/ijet.v13i12.8004. [182] Sharma, C. and Jordan, R., 2019. Is adaptive learning the way forward? Medical Teacher, 41(4), pp.484–485. URL https://doi.org/10.1080/0142159X.2018.1513644. [183] Shelle, G., Earnesty, D., Pilkenton, A. and Powell, E., 2018. Adaptive learning: An innovative method for online teaching and learning. Journal of Extension, 56(5), p.17. URL https://doi.org/10.34068/joe.56.05.17. [184] Shemshack, A., 2022. Personalized Adaptive Teacher Education to Increase SelfEfficacy: Toward a Framework for Teacher Education. PhD thesis. University of North Texas. URL https://digital.library.unt.edu/ark:/67531/metadc1944377/. [185] Shikano, S. and Kittel, B., 2016. Dynamics of Voting Propensity: Experimental Tests of Adaptive Learning Models. Political Research Quarterly, 69(4), pp.813– 829. URL https://doi.org/10.1177/1065912916663654. [186] Shyshkina, M.P., 2020. Adaptyvna khmaro oriientovana systema navchannia ta profesiinoho rozvytku vchyteliv zakladiv zahalnoi serednoi osvity [Adaptive cloudoriented system of training and professional development of teachers of general secondary education institutions]. (0221U102106). National Academy of Pedagogical Sciences of Ukraine. URL https://nrat.ukrintei.ua/searchdoc/0221U102106. [187] Skuballa, I.T., Leber, J., Schmidt, H., Zimmermann, G. and Renkl, A., 2016. Using online eye-movement analyses in an adaptive learning environment. In: L. Lin and R.K. Atkinson, eds. Educational Technologies: Challenges, Applications and Learning Outcomes. Nova Science Publishers, Inc., Education in a Competitive and Globalizing World, pp.115–142. URL https://www.researchgate.net/publication/ 316062339. [188] Slater, S., Ocumpaugh, J., Baker, R., Li, J. and Labrum, M., 2018. Identifying changes in math identity through adaptive learning systems use. In: M.M.T. Rodrigo, J.C. Yang, L.H. Wong and M. Chang, eds. ICCE 2018 - 26th International Conference on Computers in Education, Main Conference Proceedings. Asia-Pacific Society for Computers in Education, pp.71–76. URL https://learninganalytics.upenn.edu/ ryanbaker/i_paper_93.pdf. [189] Smith, K., 2018. Perceptions of preservice teachers about adaptive learning programs in K-8 mathematics education. Contemporary Educational Technology, 9(2), pp.111–130. URL https://doi.org/10.30935/cet.414780. [190] Sosnovsky, S. and Chacon, I.A., 2014. Semantic gap detection in metadata of adaptive learning environments. In: D.G. Sampson, M.J. Spector, N.S. Chen, R. Huang and Kinshuk, eds. Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014. Institute of Electrical and Electronics Engineers Inc., pp.548–552. URL https://doi.org/10.1109/ICALT.2014.161. [191] Stewart, C. and Wolodko, B., 2016. University Educator Mindsets: How Might Adult Constructive-Developmental Theory Support Design of Adaptive Learning? Mind, Brain, and Education, 10(4), pp.247–255. URL https://doi.org/10.1111/mbe. 12126. [192] Su, C.H., 2017. Designing and developing a novel hybrid adaptive learning path recommendation system (ALPRS) for gamification mathematics geometry course. Eurasia Journal of Mathematics, Science and Technology Education, 13(6), pp.2275–2298. URL https://doi.org/10.12973/EURASIA.2017.01225A. [193] Sun, Q., Norman, T.J. and Abdourazakou, Y., 2018. Perceived value of interactive digital textbook and adaptive learning: Implications on student learning effectiveness. Journal of Education for Business, 93(7), pp.322–330. URL https://doi.org/10.1080/08832323.2018.1493422. [194] Surahman, E., Kuswandi, D., Wedi, A., Degeng, I.N.S., Setyanti, D.A. and Thaariq, Z.Z.A., 2019. Adaptive learning analytics management system (Alams): An innovative online learning approach. International Journal of Innovation, Creativity and Change, 5(4), pp.413–430. [195] Szijarto, B. and Cousins, J.B., 2019. Making Space for Adaptive Learning. American Journal of Evaluation, 40(2), pp.160–176. URL https://doi.org/10.1177/ 1098214018781506. [196] Tackett, S., Raymond, M., Desai, R., Haist, S.A., Morales, A., Gaglani, S. and Clyman, S.G., 2018. Crowdsourcing for assessment items to support adaptive learning. Medical Teacher, 40(8), pp.838–841. URL https://doi.org/10.1080/ 0142159X.2018.1490704. [197] Tamo-Vargas, G., Quispe-Pari, E., Bedregal-Alpaca, N., Guevara, K., DelgadoBarra, L. and Laura-Ochoa, L., 2023. Design and development of a Moodle Plugin for the adaptation of the teaching-learning process through graded grading constraints; [Diseno y desarrollo de un Plugin en Moodle para la ˜ adaptacion de proceso ense ´ nanza-aprendizaje a trav ˜ es de restricciones de ´ calificacion escalonada]. ´ RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao. Associacao Iberica de Sistemas e Tecnologias de Informacao, vol. 2023, pp.338–351. URL https://pure.unsa.edu.pe/en/publications/dise% C3%B1o-y-desarrollo-de-un-plugin-en-moodle-para-la-adaptaci%C3%B3n-de-/fingerprints/. [198] Tashiro, J.S., Hung, P.C.K., Martin, M.V., Brown, A.L. and Hurst, F.M., 2016. Personalised-adaptive learning - An operational framework for developing competency-based curricula in computer information technology. International Journal of Innovation and Learning, 19(4), pp.412–430. URL https://doi.org/10. 1504/IJIL.2016.076793. [199] Tonkonohyy, V.M., 2013. Theory and practice of implementation of computer technologies of teaching and knowledge control of special technical disciplines using adaptive technologies. (0214U007002). Odessa National Polytechnic University. URL https://nrat.ukrintei.ua/searchdoc/0214U007002. [200] Torrano, F. and Gonzalez-Torres, M.C., 2016. Initial study of the psychometric ´ properties of the motivational scales of the PALS (Patterns of Adaptive Learning Scales) focusing on the student [Estudio inicial de las propiedades psicometricas ´ de las escalas motivacionales del PALS (Patterns of Adaptive Learning Scales) centradas en el alumno]. Estudios Pedagogicos, 42(3), pp.391–412. URL https: //doi.org/10.4067/s0718-07052016000400021. [201] Tortorella, R.A.W. and Graf, S., 2017. Considering learning styles and contextawareness for mobile adaptive learning. Education and Information Technologies, 22(1), pp.297–315. URL https://doi.org/10.1007/s10639-015-9445-x. [202] Tsai, M.H. and Huang, C.H., 2019. Application of data mining theory to investigate factors impacting high school students’ adaptive learning in Taiwan. Contemporary Educational Research Quarterly, 27(2), pp.39–76. URL https://doi.org/10.6151/ CERQ.201906_27(2).0002. [203] Ueno, H., Kato, T., Fukamachi, K., Tateno, H., Yamakawa, H. and Komatsugawa, H., 2018. The Case Study of a Flipped Classroom Using an Adaptive Learning System. In: M.M.T. Rodrigo, S. Amalathas, A.D. Coronel, J.C. Yang, Y. Song, J. Ding, M. Chang and L.H. Wong, eds. ICCE 2018 - 26th International Conference on Computers in Education, Work-in-Progress Poster Proceedings. Asia-Pacific Society for Computers in Education, pp.4–6. URL http://tinyurl.com/3cd67kxt. [204] Ueno, H., Kato, T., Yoshida, F., Tsukada, N., Tateno, H., Fukamachi, K., Yamakawa, H. and Komatsugawa, H., 2018. A model of flipped classroom using an adaptive learning system. In: J.C. Yang, C. Yin, W. Chen, A. Mohd Ayub, A. Mitrovic, J. Ding, S. Amalathas and S.L. Wong, eds. ICCE 2017 - 25th International Conference on Computers in Education: Technology and Innovation: Computer-Based Educational Systems for the 21st Century, Work In Progress Posters Proceedings. Asia-Pacific Society for Computers in Education, pp.1–3. [205] Verkhovna Rada of Ukraine, 2014. Law of Ukraine On Higher Education. URL https://zakon.rada.gov.ua/laws/show/1556-18?lang=en#Text. [206] Verkhovna Rada of Ukraine, 2018. Law of Ukraine On Education. URL https: //zakon.rada.gov.ua/laws/show/2145-19?lang=en#Text. [207] Vitez, A., 2022. Course module instances report. URL https://moodle.org/plugins/ report_coursemodstats. [208] Vo, K. and Dutkiewicz, E., 2019. Optimal length-constrained segmentation and subject-adaptive learning for real-time arrhythmia detection. Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018. Institute of Electrical and Electronics Engineers Inc., vol. 2019-January, pp.112–119. URL https://doi.org/10.1145/3278576. 3284385. [209] Vu Minh, C., 2005. Constructivist learning: an operational approach for designing adaptive learning environments supporting cognitive flexibility. PhD thesis. Universite Catholique de Louvain. URL http://hdl.handle.net/2078.1/5145. ´ [210] Vyshnevska, V.M., 2007. The system of adaptive studies is on principles of fuzzy logic. Dissertation is on the receipt of scientific degree of candidate of engineerings sciences after speciality 05.13.06 – Control the system and progressive information technologies is automated. Odessa national polytechnic university, Odessa. URL https://nrat.ukrintei.ua/searchdoc/0407U004559. [211] Vasquez-Berm ´ udez, M., Aguirre-Munizaga, M. and Hidalgo-Larrea, J., 2023. ´ Analysis of CoI Presence Indicators in a Moodle Forum Using Unsupervised Learning Techniques. In: R. Valencia-Garc´ıa, M. Bucaram-Leverone, J. Del Cioppo-Morstadt, N. Vera-Lucio and P.H. Centanaro-Quiroz, eds. Communications in Computer and Information Science. Springer Science and Business Media Deutschland GmbH, vol. 1873 CCIS, pp.27 – 38. URL https://doi.org/10.1007/ 978-3-031-45682-4_3. [212] Walkington, C. and Sherman, M., 2012. Using adaptive learning technologies to personalize instruction: The impact of interest-based scenarios on performance in Algebra. 10th International Conference of the Learning Sciences: The Future of Learning, ICLS 2012 - Proceedings. vol. 1, pp.80–87. URL https://doi.org/10.22318/ icls2012.1.80. [213] Walkington, C.A., 2013. Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), pp.932–945. URL https://doi.org/10.1037/a0031882. [214] Wang, S., Feng, M., Bienkowski, M., Christensen, C. and Cui, W., 2019. Learning from an adaptive learning system: Student profiling among middle school students. In: H. Lane, S. Zvacek and J. Uhomoibhi, eds. CSEDU 2019 - Proceedings of the 11th International Conference on Computer Supported Education. SciTePress, vol. 1, pp.78–84. URL https://doi.org/10.5220/0007729700780084. [215] Wang, Y.H. and Liao, H.C., 2011. Adaptive learning for ESL based on computation. British Journal of Educational Technology, 42(1), pp.66–87. URL https://doi.org/10. 1111/j.1467-8535.2009.00981.x. [216] Welsh, S. and Uys, P., 2019. Dreaming of electric sheep: CSU’s vision for analyticsdriven adaptive learning and teaching. ASCILITE 2015 - Australasian Society for Computers in Learning and Tertiary Education, Conference Proceedings. Australasian Society for Computers in Learning in Tertiary Education (ASCILITE), pp.593–598. URL https://www.researchgate.net/publication/290411880. [217] Werlen, E. and Bergamin, P., 2018. Self-evaluation of open answers as a basis for adaptive learning systems. In: D.G. Sampson, P. Isaias, D. Ifenthaler and L. Rodrigues, eds. Proceedings of the 15th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2018. IADIS Press, pp.335–340. URL https://www.researchgate.net/publication/326957895. [218] Yang, T.C., Hwang, G.J. and Yang, S.J.H., 2013. Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Educational Technology and Society, 16(4), pp.185–200. URL https://www.jstor.org/stable/jeductechsoci.16.4.185. [219] Yang, Y.T.C., Gamble, J.H., Hung, Y.W. and Lin, T.Y., 2014. An online adaptive learning environment for critical-thinking-infused English literacy instruction. British Journal of Educational Technology, 45(4), pp.723–747. URL https: //doi.org/10.1111/bjet.12080. [220] Yeti¸sir, M.˙ I. and Ceylan, E., 2015. The adaptation of students’ adaptive learning engagement in science scale into Turkish [Fen O¨ grenimine y ˘ onelim ¨ Ol¸ce ¨ ginin ˘ turk¸ce’ ye uyarlanması]. ¨ Elementary Education Online, 14(2), pp.657–670. URL https://doi.org/10.17051/io.2015.49510. [221] Yuan, R.Q., Hsieh, S.W., Chew, S.W. and Chen, N.S., 2016. The Effects of Gesture-Based Technology on Memory Training in Adaptive Learning Environment. In: L. Chen, C.C. Tsai, H.H. Yang, H.H. Yang, X. Yu, Q. Liu, S. Wang and J. Cheng, eds. Proceedings - 2015 International Conference of Educational Innovation Through Technology, EITT 2015. Institute of Electrical and Electronics Engineers Inc., pp.190–193. URL https://doi.org/10.1109/EITT.2015.47. [222] Yurchenko, K.M., 2012. Adaptive technology of educating and determination for professional preparedness level of operatively-rescue service specialists. Thesis for Ph.D degree in the specialty 05.13.06 – Information technologies. Cherkasy State Technological University, Cherkasy. URL https://nrat.ukrintei.ua/searchdoc/ 0412U004271. [223] Zhang, J.C., 2016. Adaptive learning environment system based on multi-event driven technology. International Journal of Emerging Technologies in Learning, 11(11), pp.37–42. URL https://doi.org/10.3991/ijet.v11i11.6250. [224] Zhao, L. and Wang, H., 2019. Research on adaptive learning system based on three core modules. Proceedings - 10th International Conference on Information Technology in Medicine and Education, ITME 2019. Institute of Electrical and Electronics Engineers Inc., pp.447–452. URL https://doi.org/10.1109/ITME.2019.00106. [225] Zhou, Z., Zhang, Q., Lu, G., Wang, H., Zhang, W. and Yu, Y., 2019. Adashift: Decorrelation and convergence of adaptive learning rate methods. 7th International Conference on Learning Representations, ICLR 2019. International Conference on Learning Representations, ICLR. URL https://doi.org/10.48550/arXiv.1810.00143. [226] Ziimpe, M., 2011. Determination, adaptive learning and monetary policy rules in the new canonical model: A primer [Determination, apprentissage adaptatif et ´ regles de politique mon ` etaire dans le nouveau mod ´ ele canonique: Un guide de ` lecture]. Revue d’Economie Politique, 121(3), pp.307–345. URL https://doi.org/10. 3917/redp.213.0307. [227] Zulfiani, Z., Suwarna, I.P. and Miranto, S., 2018. Science education adaptive learning system as a computer-based science learning with learning style variations. Journal of Baltic Science Education, 17(4), pp.711–727. URL https://doi.org/10. 33225/jbse/18.17.711. | uk |
| dc.description.abstract | Інформаційно-когнітивні технології, такі як штучний інтелект, людино- машинна взаємодія, когнітивні обчислення, візуалізація інформації та управління знаннями, спрямовані на розробку систем, інструментів та інтерфейсів, які безшовно інтегруються з когнітивними процесами людини та доповнюють їх. Інформаційно-когнітивні технології відіграють дедалі важливішу роль в освіті, уможливлюючи персоналізоване навчання та покращуючи набуття знань. Такі інформаційно-когнітивні технології в освіті, як адаптивне навчання (яскравий приклад застосування штучного інтелекту в освіті для підтримки персоналізованого навчання), мають потенціал для покращення освіти через персоналізоване навчання. Аналіз великих освітніх даних, що генеруються у процесі використання систем управління навчанням, таких як Moodle, надає можливість виявлення закономірностей, ідей та тенденцій, що дозволять викладачам та адміністраторам приймати рішення на основі даних. Дослідження присвячене використанню системи управління навчанням (LMS) Moodle та її потенціалу для впровадження адаптивного навчання в педагогічних університетах. Використовуючи багатогранний підхід, який поєднує бібліометричний аналіз, моделювання структурних рівнянь та емпіричні дані з Криворізького державного педагогічного університету, дослідження забезпечує глибоке розуміння тематичних кластерів у рамках дослідження адаптивного навчання, взаємозв’язку інструментів Moodle, їхнього впливу на результати навчання студентів та надає рекомендації із упровадження ефективних стратегій адаптивного навчання. Завдяки бібліометричному огляду визначені п’ять основних тематичних кластерів, які характеризують дослідження із адаптивного навчання у вищій освіті: (1) загальні концепції адаптивного навчання в системах електронного навчання, (2) освітні технології, (3) адаптивні системи навчання та педагогічна інформатика, (4) навчання та дослідження в галузі освіти та (5) персоналізоване навчання. Аналіз показав, що ці кластери тісно взаємопов’язані, відображаючи багатовимірну та динамічну природу досліджень адаптивного навчання. Досягнення або відкриття в одному кластері можуть мати наслідки та сприяти розвитку в інших кластерах, підкреслюючи міждисциплінарний характер цієї галузі. Зокрема, аналіз підкреслив ключову роль технологій штучного інтелекту в розробці адаптивних систем навчання, а також важливість персоналізованої освіти та урахування індивідуальних стилів навчання. Щоб дослідити взаємозв’язок ресурсів і діяльності Moodle, а також їхній вплив на результати навчання студентів, було застосовано кількісний підхід – моделювання структурних рівнянь (SEM-PLS). Використовуючи дані Криворізького державного педагогічного університету, було розроблено концептуальну модель, засновану на соціально-конструкційній педагогіці, покладеної в основі розробки Moodle, і положеннях університету щодо структури курсу та оцінювання Moodle. Модель складалася з п’яти конструктів: “Інформація”, “Ресурси”, “Діяльність”, “Комунікація” та “Оцінка”. Конструкти “Інформація”, “Ресурси”, “Діяльність” і “Комунікація” були початковими конструктами, сформованими з використанням індикаторів, які були безпосередньо з даних Moodle, тоді як конструкт “Оцінка” по відношенню до них був прихованим конструктом, що відображав дані про успішність студентів, узяті із заліково-екзаменаційних відомостей. Результати моделювання надали можливість зробити декілька ключових висновків. По-перше, існує сильний позитивний зв’язок між конструктом “Діяльність” (інтерактивні навчальні елементи, такі як SCORM, вибір, завдання, тести) та конструктом “Комунікація”, що свідчить про те, що збільшення використання інтерактивних елементів у курсів Moodle пов’язане з вищим рівнем спілкування та взаємодії студентів і викладачів. По-друге, спостерігався помірний позитивний зв’язок між конструктом “Ресурси” (URL-адреси, сторінки, мітки тощо) і конструктом “Діяльність”, що вказує на те, що доступність і різноманітність ресурсів у межах курсу Moodle пов’язані з включенням різноманітних навчальних дій. По-третє, було виявлено помірний позитивний зв’язок між конструктом “Інформація” (опис курсу, програма, вступ) і конструктом “Оцінка” (оцінки студента), що свідчить про те, що добре розроблені та інформативні матеріали курсу пов’язані з кращими результатами оцінювання студентів. Цікаво, що дослідження не знайшло доказів значного прямого зв’язку між комунікацією чи діяльністю та конструктом “Оцінка”. Незважаючи на те, що це важливі компоненти, їхній вплив на результати оцінювання є більш складним і на нього впливають інші фактори. Головним висновком цього дослідження є те, що просте використання засобів Moodle не гарантує впровадження адаптивного навчання для студентів педагогічних університетів. У той час як Moodle надає інструменти та функції, які потенційно можуть підтримувати адаптивне навчання, такі як персоналізовані навчальні траєкторії, адаптивне надання контенту та навчальна аналітика, наявність і використання цих інструментів самі по собі не гарантують ефективного впровадження адаптивного навчання. Щоб по- справжньому використати потенціал адаптивного навчання, викладачі та розробники курсів повинні застосувати продуманий і стратегічний підхід, який передбачає ретельне навчальне проєктування, інтеграцію відповідних педагогічних стратегій і використання адаптивних можливостей Moodle у відповідності до конкретних цілей навчання та потреб студентів. Наукова новизна результатів дослідження полягає в наступному: 1. Бібліометричний аналіз джерел з проблеми дослідження надав можливість визначити ключові тематичні кластери та тенденції, виявивши багатовимірний та взаємопов’язаний характер досліджень у галузі адаптивного навчання. 2. Розробка та застосування кількісної моделі SEM-PLS для дослідження зв’язків між різними ресурсами Moodle LMS, діяльністю та результатами оцінювання студентів надала уявлення про взаємозв’язок різних інструментів Moodle, які використовують викладачі в педагогічних університетах. 3. Існують емпіричні докази впливу різних інструментів Moodle на результати навчання студентів, зокрема сильний позитивний зв’язок між інтерактивною діяльністю та спілкуванням/залученням, помірний позитивний зв’язок між ресурсами та навчальною діяльністю, а також помірний позитивний зв’язок між інформацією про курс і результатами оцінювання. 4. Висновок про те, що просте використання інструментів Moodle не гарантує впровадження адаптивного навчання для студентів педагогічних університетів, підкреслює необхідність ретельного проєктування навчання, інтеграції відповідних педагогічних стратегій і педагогічно виваженого використання адаптивних можливостей Moodle. Практичне значення отриманих результатів полягає в наданні рекомендацій для викладачів і розробників курсів щодо оптимізації використання Moodle LMS шляхом акцентування уваги на інтерактивних заходах, різноманітних ресурсах, вичерпній інформації про курси та стратегічній інтеграції адаптивних можливостей Moodle для підвищення взаємодії студентів, спілкування та результати навчання в педагогічних університетах. | uk |
| dc.language.iso | uk | uk |
| dc.publisher | Криворізький державний педагогічний університет | uk |
| dc.subject | інформаційно-когнітивні технології | uk |
| dc.subject | адаптивне навчання | uk |
| dc.subject | системи управління навчанням | uk |
| dc.subject | Moodle | uk |
| dc.subject | педагогічні університети | uk |
| dc.subject | результати навчання студентів | uk |
| dc.subject | інтерактивна діяльність | uk |
| dc.subject | навчальні ресурси | uk |
| dc.subject | навчальна аналітика | uk |
| dc.subject | персоналізоване навчання | uk |
| dc.subject | освітня технологія | uk |
| dc.subject | моделювання структурними рівняннями | uk |
| dc.subject | бібліометричний аналіз | uk |
| dc.subject | взаємозв’язок інструментів | uk |
| dc.subject | дизайн курсу | uk |
| dc.subject | спілкування | uk |
| dc.subject | оцінка | uk |
| dc.subject | продуктивність | uk |
| dc.subject | методика використання інформаційно-когнітивних технологій адаптивного навчання студентів педагогічних університетів | uk |
| dc.title | Методика використання інформаційно-когнітивних технологій адаптивного навчання студентів педагогічних університетів | uk |
| dc.type | Thesis | uk |
