Approaches to the choice of tools for adaptive learning based on highlighted selection criteria

Sikora, Yaroslava B.; Usata, Olena Yu.; Mosiiuk, Oleksandr O.; Verbivskyi, Dmytrii S.; Shmeltser, Ekaterina O.

URI: http://ceur-ws.org/Vol-2879/paper22.pdf
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4447
https://doi.org/10.31812/123456789/4447

Date: 2021-06-10

Abstract:

The article substantiates the relevance of adaptive learning of students in the modern information society, reveals the essence of such concepts as “adaptability” and “adaptive learning system”. It is determined that a necessary condition for adaptive education is the criterion of an adaptive learning environment that provides opportunities for advanced education, development of key competencies, formation of a flexible personality that is able to respond to different changes, effectively solve different problems and achieve results. The authors focus on the technical aspect of adaptive learning. Different classifications of adaptability are analyzed. The approach to the choice of adaptive learning tools based on the characteristics of the product quality model stated by the standard ISO / IEC 25010 is described. The following criteria for the selecting adaptive learning tools are functional compliance, compatibility, practicality, and support. By means of expert assessment method there were identified and selected the most important tools of adaptive learning, namely: Acrobatiq, Fishtree, Knewton (now Wiliy), Lumen, Realize it, Smart Sparrow (now Pearson). Comparative tables for each of the selected tools of adaptive learning according to the indicators of certain criteria are given.

Description:

[1] V. Ustinova, S. Shokaliuk, I. Mintii, A. Pikilnyak, Modern techniques of organizing computer support for future teachers’ independent work in German language, CEUR Workshop Proceedings 2433 (2019) 308–321. [2] S. Shokaliuk, Y. Bohunenko, I. Lovianova, M. Shyshkina, Technologies of distance learning for programming basics on the principles of integrated development of key competences, CEUR Workshop Proceedings 2643 (2020) 548–562. [3] I. Mintii, S. Shokaliuk, T. Vakaliuk, M. Mintii, V. Soloviev, Import test questions into Moodle LMS, CEUR Workshop Proceedings 2433 (2019) 529–540. [4] I. Mintii, Using Learning Content Management System Moodle in Kryvyi Rih State Pedagogical University educational process, CEUR Workshop Proceedings 2643 (2020) 293–305. [5] A. Abdula, H. Baluta, N. Kozachenko, D. Kassim, Peculiarities of using of the Moodle test tools in philosophy teaching, CEUR Workshop Proceedings 2643 (2020) 306–320. [6] M. Syvyi, O. Mazbayev, O. Varakuta, N. Panteleeva, O. Bondarenko, Distance learning as innovation technology of school geographical education, CEUR Workshop Proceedings 2731 (2020) 369–382. [7] K. Polhun, T. Kramarenko, M. Maloivan, A. Tomilina, Shift from blended learning to distance one during the lockdown period using Moodle: test control of students’ academic achievement and analysis of its results, Journal of Physics: Conference Series 1840 (2021) 012053. URL: https://doi.org/10.1088/1742-6596/1840/1/012053. doi:10.1088/1742-6596/1840/1/012053. [8] V. Tkachuk, Y. Yechkalo, S. Semerikov, M. Kislova, Y. Hladyr, Using Mobile ICT for Online Learning During COVID-19 Lockdown, in: A. Bollin, V. Ermolayev, H. C. Mayr, M. Nikitchenko, A. Spivakovsky, M. Tkachuk, V. Yakovyna, G. Zholtkevych (Eds.), Information and Communication Technologies in Education, Research, and Industrial Applications, Springer International Publishing, Cham, 2021, pp. 46–67. [9] O. Haranin, N. Moiseienko, Adaptive artificial intelligence in RPG-game on the Unity game engine, CEUR Workshop Proceedings 2292 (2018) 143–150. [10] M. Petrova, M. Mintii, S. Semerikov, N. Volkova, Development of adaptive educational software on the topic of “Fractional Numbers” for students in grade 5, CEUR Workshop Proceedings 2292 (2018) 162–192. [11] P. Fedoruk, Technology for construction of individual learning trajectory using knowledge script examples in adaptive systems of distance learning, Cambridge, 2011, pp. 88–92. doi:10.2316/P.2011.734-021. [12] O. S. Diahyleva, I. V. Gritsuk, O. Y. Kononova, A. Y. Yurzhenko, Computerized adaptive testing in educational electronic environment of maritime higher education institutions, CEUR Workshop Proceedings (2020, in press). [13] Y. Tyshchenko, A. Striuk, The relevance of developing a model of adaptive learning, CEUR Workshop Proceedings 2292 (2018) 109–115. [14] N. Shapovalova, O. Rybalchenko, I. Dotsenko, S. Bilashenko, A. Striuk, L. Saitgareev, Adaptive testing model as the method of quality knowledge control individualizing, CEUR Workshop Proceedings 2393 (2019) 984–999. [15] V. Osadchyi, I. Krasheninnik, O. Spirin, S. Koniukhov, T. Diuzhikova, Personalized and adaptive ICT-enhanced learning: a brief review of research from 2010 to 2019, in: CEUR Workshop Proceedings, volume 2732, 2020, pp. 559–571. URL: http://ceur-ws.org/Vol-2732/ 20200559.pdf. [16] M. Marienko, Y. Nosenko, A. Sukhikh, V. Tataurov, M. Shyshkina, Personalization of learning through adaptive technologies in the context of sustainable development of teachers’ education, E3S Web of Conferences 166 (2020) 10015. doi:10.1051/e3sconf/ 202016610015. [17] Y. Nosenko, M. Popel, M. Shyshkina, The state of the art and perspectives of using adaptive cloud-based learning systems in higher education pedagogical institutions (the scope of ukraine), CEUR Workshop Proceedings 2433 (2019) 173–183. URL: http://ceur-ws.org/ Vol-2433/paper10.pdf, 6th Workshop on Cloud Technologies in Education, CTE 2018 ; Conference Date: 21 December 2018. [18] H. Chemerys, K. Osadcha, V. Osadchyi, I. Naumuk, H. Ustiuhova, Analysis of ergonomic indicators and compliance with the principles of the instructional design of education courses in adaptive learning systems, CEUR Workshop Proceedings 2732 (2020) 619–633. URL: http://ceur-ws.org/Vol-2732/20200619.pdf. [19] K. Osadcha, V. Osadchyi, S. Semerikov, H. Chemerys, A. Chorna, The review of the adaptive learning systems for the formation of individual educational trajectory, CEUR Workshop Proceedings 2732 (2020) 547–558. URL: http://ceur-ws.org/Vol-2732/20200547.pdf. [20] V. Osadchyi, H. Chemerys, K. Osadcha, V. Kruhlyk, S. Koniukhov, A. Kiv, Conceptual model of learning based on the combined capabilities of augmented and virtual reality technologies with adaptive learning systems, CEUR Workshop Proceedings 2731 (2020) 328–340. [21] N. Morze, L. Varchenko-Trotsenko, T. Terletska, E. Smyrnova-Trybulska, Implementation of adaptive learning at higher education institutions by means of Moodle LMS, Journal of Physics: Conference Series 1840 (2021) 012062. URL: https://doi.org/10.1088/1742-6596/ 1840/1/012062. doi:10.1088/1742-6596/1840/1/012062. [22] C. M. Liu, Y. J. Sun, H. Y. Li, Adaptive learning system designed and learning program optimization algorithm, in: Applied Mechanics and Materials, volume 347, Trans Tech Publ, 2013, pp. 3114–3118. doi:10.4028/www.scientific.net/amm.347-350.3114. [23] A.-M. Mirea, M. C. Preda, Adaptive learning based on exercises fitness degree, in: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, volume 3, IEEE, 2009, pp. 215–218. doi:10.1109/WI-IAT.2009.266. [24] E.-N. El Mokhtar, Z. Abdelhamid, et al., Hybrid approach of the fuzzy c-means and the k-nearest neighbors methods during the retrieve phase of dynamic case based reasoning for personalized follow-up of learners in real time, International Journal of Electrical and Computer Engineering 9 (2019) 4939–4950. doi:10.11591/ijece.v9i6.pp4939-4950. [25] N. D. Wirastuti, G. Sukadarmika, I. A. Suyadnya, D. C. Krishne, et al., Adaptive online learning design using Moodle, in: 2016 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS), IEEE, 2016, pp. 98–101. doi:10.1109/ICSGTEIS.2016.7885773. [26] C.-H. Wu, T.-C. Chen, Y.-H. Yan, C.-F. Lee, Developing an adaptive e-learning system for learning Excel, in: 2017 International Conference on Applied System Innovation (ICASI), IEEE, 2017, pp. 1973–1975. doi:10.1109/ICASI.2017.7988583. [27] A. T. Bimba, N. Idris, A. Al-Hunaiyyan, R. B. Mahmud, N. L. B. M. Shuib, Adaptive feedback in computer-based learning environments: a review, Adaptive Behavior 25 (2017) 217–234. doi:10.1177/1059712317727590. [28] M. Shyshkina, Perspective technologies of e-learning systems development, Information technologies in education (2011) 132–139. [29] S. M. Honcharov, A. A. Biletskyi, O. M. Hubnytska, T. A. Kostiukova, Formy, metody i orhanizatsiia navchalnoho protsesu v kredytno-modulnii system, NUVHTT, Rivne, 2007. [30] N. Moiseienko, M. Moiseienko, S. Semerikov, Mobilne informatsiino-osvitnie seredovyshche vyshchoho navchalnoho zakladu (The mobile information and educational environment of higher educational institution), Bulletin of the Cherkasy Bohdan Khmelnytsky National University. Series "Pedagogical Sciences 11 (2016) 20–27. URL: http://ped-ejournal.cdu.edu.ua/article/view/1610/1673. [31] V. Borogev, Psikhologo-pedagogicheskie osnovy sistemy adaptivnogo obucheniia (Psychological and pedagogical foundations of the system of adaptive learning), Nauka i shkola (2001) 12–15. [32] B. F. Skinner, Science and human behavior, Simon and Schuster, 1965. URL: http://www. bfskinner.org/newtestsite/wp-content/uploads/2014/02/ScienceHumanBehavior.pdf. [33] N. A. Krauder, O razlichiyakh mezhdu linejnym i razvetvlyonnym programmirovaniem (on the differences between linear and branched programming), in: I. I. Tikhonov (Ed.), Programmirovannoe obuchenie za rubezhom, Vysshaya shkola, Moskva, 1968, pp. 58–67. [34] G. Pask, The cybernetics of human learning and performance: A guide to theory and research, Hutchinson Educational, London, 1975. [35] W. Stangl, Stichwort: Adaptivität | online lexikon für psychologie und pädagogik, 2019. URL: https://lexikon.stangl.eu/982/adaptivitaet/. [36] L. Pugliese, Adaptive learning systems: Surviving the storm, 2016. URL: https://er.educause. edu/articles/2016/10/adaptive-learning-systems-surviving-the-storm. [37] H. Khosravi, S. Sadiq, D. Gasevic, Development and adoption of an adaptive learning system: Reflections and lessons learned, in: Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 2020, pp. 58–64. doi:10.1145/3328778.3366900. [38] Decoding adaptive, 2016. URL: https://www.pearson.com/content/ dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/ Pearson-Decoding-Adaptive-v5-Web.pdf. [39] M. S. Mazorchuk, T. S. Vakulenko, A. O. Bychko, O. H. Kuzminska, O. V. Prokhorov, Cloud technologies and learning analytics: web application for PISA results analysis and visualization, CEUR Workshop Proceedings (2020, in press). [40] G. Bryant, Learning to adapt 2.0: The evolution of adaptive learning in higher education, 2016. URL: https://tytonpartners.com/library/ learning-to-adapt-2-0-the-evolution-of-adaptive-learning-in-higher-education/. [41] O. Pursky, T. Dubovyk, I. Gamova, I. Buchatska, Computation algorithm for integral indicator of socio-economic development, volume 2393, CEUR-WS, 2019. [42] ISO/IEC 25010:2011. Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — System and software quality models, ISO, 2011. URL: https://www.iso.org/standard/35733.html.

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