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

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

doi:10.55056/cte.296

Authors

Yaroslava B. Sikora Zhytomyr Ivan Franko State University https://orcid.org/0000-0003-2621-6638
Olena Yu. Usata Zhytomyr Ivan Franko State University https://orcid.org/0000-0002-0610-7007
Oleksandr O. Mosiiuk Zhytomyr Ivan Franko State University https://orcid.org/0000-0003-3530-1359
Dmytrii S. Verbivskyi Zhytomyr Ivan Franko State University https://orcid.org/0000-0002-5238-1189
Ekaterina O. Shmeltser State University of Economics and Technology https://orcid.org/0000-0001-6830-8747

DOI:

https://doi.org/10.55056/cte.296

Keywords:

adaptability, adaptive learning, adaptive learning tools, selection criteria

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 Wiley), 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.

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References

Abdula, A., Baluta, H., Kozachenko, N. and Kassim, D., 2020. Peculiarities of using of the Moodle test tools in philosophy teaching. Ceur workshop proceedings, 2643, pp.306–320. DOI: https://doi.org/10.55056/cte.362

Bimba, A.T., Idris, N., Al-Hunaiyyan, A., Mahmud, R.B. and Shuib, N.L.B.M., 2017. Adaptive feedback in computer-based learning environments: a review. Adaptive behavior, 25(5), pp.217–234. Available from: https://doi.org/10.1177/1059712317727590. DOI: https://doi.org/10.1177/1059712317727590

Borogev, V., 2001. Psikhologo-pedagogicheskie osnovy sistemy adaptivnogo obucheniia (Psychological and pedagogical foundations of the system of adaptive learning). Nauka i shkola, (2), pp.12–15.

Bryant, G., 2016. Learning to adapt 2.0: The evolution of adaptive learning in higher education. Available from: https://tytonpartners.com/library/learning-to-adapt-2-0-the-evolution-of-adaptive-learning-in-higher-education/

Chemerys, H., Osadcha, K., Osadchyi, V., Naumuk, I. and Ustiuhova, H., 2020. Analysis of ergonomic indicators and compliance with the principles of the instructional design of education courses in adaptive learning systems. Ceur workshop proceedings, 2732, pp.619–633. Available from: http://ceur-ws.org/Vol-2732/20200619.pdf.

Decoding adaptive, 2016. Available from: https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/Pearson-Decoding-Adaptive-v5-Web.pdf.

Diahyleva, O.S., Gritsuk, I.V., Kononova, O.Y. and Yurzhenko, A.Y., 2020, in press. Computerized adaptive testing in educational electronic environment of maritime higher education institutions. Ceur workshop proceedings. DOI: https://doi.org/10.31812/123456789/4448

El Mokhtar, E.N., Abdelhamid, Z. et al., 2019. 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(6), pp.4939–4950. Available from: https://doi.org/10.11591/ijece.v9i6.pp4939-4950. DOI: https://doi.org/10.11591/ijece.v9i6.pp4939-4950

Fedoruk, P., 2011. Technology for construction of individual learning trajectory using knowledge script examples in adaptive systems of distance learning. Cambridge, pp.88–92. Available from: https://doi.org/10.2316/P.2011.734-021. DOI: https://doi.org/10.2316/P.2011.734-021

Haranin, O. and Moiseienko, N., 2018. Adaptive artificial intelligence in RPG-game on the Unity game engine. Ceur workshop proceedings, 2292, pp.143–150. DOI: https://doi.org/10.31812/123456789/2891

Honcharov, S.M., Biletskyi, A.A., Hubnytska, O.M. and Kostiukova, T.A., 2007. Formy, metody i orhanizatsiia navchalnoho protsesu v kredytno-modulnii system. Rivne: NUVHTT.

ISO/IEC 25010:2011. Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — System and software quality models, 2011. ISO. Available from: https://www.iso.org/standard/35733.html.

Khosravi, H., Sadiq, S. and Gasevic, D., 2020. Development and adoption of an adaptive learning system: Reflections and lessons learned. Proceedings of the 51st acm technical symposium on computer science education. pp.58–64. Available from: https://doi.org/10.1145/3328778.3366900. DOI: https://doi.org/10.1145/3328778.3366900

Krauder, N.A., 1968. O razlichiyakh mezhdu linejnym i razvetvlyonnym programmirovaniem (on the differences between linear and branched programming). In: I.I. Tikhonov, ed. Programmirovannoe obuchenie za rubezhom. Moskva: Vysshaya shkola, pp.58–67.

Liu, C.M., Sun, Y.J. and Li, H.Y., 2013. Adaptive learning system designed and learning program optimization algorithm. Applied mechanics and materials. Trans Tech Publ, vol. 347, pp.3114–3118. Available from: https://doi.org/10.4028/www.scientific.net/amm.347-350.3114. DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.3114

Marienko, M., Nosenko, Y., Sukhikh, A., Tataurov, V. and Shyshkina, M., 2020. Personalization of learning through adaptive technologies in the context of sustainable development of teachers’ education. E3s web of conferences, 166, p.10015. Available from: https://doi.org/10.1051/e3sconf/202016610015. DOI: https://doi.org/10.1051/e3sconf/202016610015

Mazorchuk, M.S., Vakulenko, T.S., Bychko, A.O., Kuzminska, O.H. and Prokhorov, O.V., 2020, in press. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. Ceur workshop proceedings. DOI: https://doi.org/10.31812/123456789/4451

Mintii, I., 2020. Using Learning Content Management System Moodle in Kryvyi Rih State Pedagogical University educational process. Ceur workshop proceedings, 2643, pp.293–305. DOI: https://doi.org/10.55056/cte.361

Mintii, I., Shokaliuk, S., Vakaliuk, T., Mintii, M. and Soloviev, V., 2019. Import test questions into Moodle LMS. Ceur workshop proceedings, 2433, pp.529–540. DOI: https://doi.org/10.55056/cte.411

Mirea, A.M. and Preda, M.C., 2009. Adaptive learning based on exercises fitness degree. 2009 ieee/wic/acm international joint conference on web intelligence and intelligent agent technology. IEEE, vol. 3, pp.215–218. Available from: https://doi.org/10.1109/WI-IAT.2009.266. DOI: https://doi.org/10.1109/WI-IAT.2009.266

Moiseienko, N., Moiseienko, M. and Semerikov, S., 2016. 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, p.20–27. Available from: http://ped-ejournal.cdu.edu.ua/article/view/1610/1673.

Morze, N., Varchenko-Trotsenko, L., Terletska, T. and Smyrnova-Trybulska, E., 2021. Implementation of adaptive learning at higher education institutions by means of Moodle LMS. Journal of physics: Conference series, 1840(1), p.012062. Available from: https://doi.org/10.1088/1742-6596/1840/1/012062. DOI: https://doi.org/10.1088/1742-6596/1840/1/012062

Nosenko, Y., Popel, M. and Shyshkina, M., 2019. 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, pp.173–183. 6th Workshop on Cloud Technologies in Education, CTE 2018 ; Conference Date: 21 December 2018. Available from: http://ceur-ws.org/Vol-2433/paper10.pdf.

Osadcha, K., Osadchyi, V., Semerikov, S., Chemerys, H. and Chorna, A., 2020. The review of the adaptive learning systems for the formation of individual educational trajectory. Ceur workshop proceedings, 2732, pp.547–558. Available from: http://ceur-ws.org/Vol-2732/20200547.pdf. DOI: https://doi.org/10.31812/123456789/4130

Osadchyi, V., Chemerys, H., Osadcha, K., Kruhlyk, V., Koniukhov, S. and Kiv, A., 2020. Conceptual model of learning based on the combined capabilities of augmented and virtual reality technologies with adaptive learning systems. Ceur workshop proceedings, 2731, pp.328–340. DOI: https://doi.org/10.31812/123456789/4417

Osadchyi, V., Krasheninnik, I., Spirin, O., Koniukhov, S. and Diuzhikova, T., 2020. Personalized and adaptive ICT-enhanced learning: a brief review of research from 2010 to 2019. Ceur workshop proceedings. vol. 2732, pp.559–571.

Pask, G., 1975. The cybernetics of human learning and performance: A guide to theory and research. London: Hutchinson Educational.

Petrova, M., Mintii, M., Semerikov, S. and Volkova, N., 2018. Development of adaptive educational software on the topic of “Fractional Numbers” for students in grade 5. Ceur workshop proceedings, 2292, pp.162–192. DOI: https://doi.org/10.31812/123456789/2894

Polhun, K., Kramarenko, T., Maloivan, M. and Tomilina, A., 2021. 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(1), p.012053. Available from: https://doi.org/10.1088/1742-6596/1840/1/012053. DOI: https://doi.org/10.1088/1742-6596/1840/1/012053

Pugliese, L., 2016. Adaptive learning systems: Surviving the storm. Available from: https://er.educause.edu/articles/2016/10/adaptive-learning-systems-surviving-the-storm.

Pursky, O., Dubovyk, T., Gamova, I. and Buchatska, I., 2019. Computation algorithm for integral indicator of socio-economic development, vol. 2393. CEUR-WS.

Shapovalova, N., Rybalchenko, O., Dotsenko, I., Bilashenko, S., Striuk, A. and Saitgareev, L., 2019. Adaptive testing model as the method of quality knowledge control individualizing. Ceur workshop proceedings, 2393, pp.984–999. DOI: https://doi.org/10.31812/123456789/3174

Shokaliuk, S., Bohunenko, Y., Lovianova, I. and Shyshkina, M., 2020. Technologies of distance learning for programming basics on the principles of integrated development of key competences. Ceur workshop proceedings, 2643, pp.548–562. DOI: https://doi.org/10.55056/cte.412

Shyshkina, M., 2011. Perspective technologies of e-learning systems development. Information technologies in education, (10), pp.132–139. DOI: https://doi.org/10.14308/ite000277

Skinner, B.F., 1965. Science and human behavior. Simon and Schuster. Available from: http://www.bfskinner.org/newtestsite/wp-content/uploads/2014/02/ScienceHumanBehavior.pdf.

Stangl, W., 2019. Stichwort: Adaptivität | online lexikon für psychologie und pädagogik. Available from: https://lexikon.stangl.eu/982/adaptivitaet/.

Syvyi, M., Mazbayev, O., Varakuta, O., Panteleeva, N. and Bondarenko, O., 2020. Distance learning as innovation technology of school geographical education. Ceur workshop proceedings, 2731, pp.369–382. DOI: https://doi.org/10.31812/123456789/4422

Tkachuk, V., Yechkalo, Y., Semerikov, S., Kislova, M. and Hladyr, Y., 2021. 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 and G. Zholtkevych, eds. Information and communication technologies in education, research, and industrial applications. Cham: Springer International Publishing, pp.46–67. DOI: https://doi.org/10.1007/978-3-030-77592-6_3

Tyshchenko, Y. and Striuk, A., 2018. The relevance of developing a model of adaptive learning. Ceur workshop proceedings, 2292, pp.109–115.

Ustinova, V., Shokaliuk, S., Mintii, I. and Pikilnyak, A., 2019. Modern techniques of organizing computer support for future teachers’ independent work in German language. Ceur workshop proceedings, 2433, pp.308–321. DOI: https://doi.org/10.55056/cte.393

Wirastuti, N.D., Sukadarmika, G., Suyadnya, I.A., Krishne, D.C. et al., 2016. Adaptive online learning design using Moodle. 2016 international conference on smart green technology in electrical and information systems (icsgteis). IEEE, pp.98–101. Available from: https://doi.org/10.1109/ICSGTEIS.2016.7885773. DOI: https://doi.org/10.1109/ICSGTEIS.2016.7885773

Wu, C.H., Chen, T.C., Yan, Y.H. and Lee, C.F., 2017. Developing an adaptive e-learning system for learning Excel. 2017 international conference on applied system innovation (icasi). IEEE, pp.1973–1975. Available from: https://doi.org/10.1109/ICASI.2017.7988583. DOI: https://doi.org/10.1109/ICASI.2017.7988583