USING THE STUDENTS' STATE INDICES FOR DESIGN OF ADAPTIVE LEARNING SYSTEMS
AbstractThe paper discusses what scientific ideas could be used to increase effectiveness of adaptive learning systems. Possibilities to use changes of learner's cognitive state indicators under influence of internal (heart rate and blood pressure) and external (speed and density of solar wind) factors are discussed. It is described experience of use of the learner's cognitive state assessment (accounting psychological, physiological and external parameters) to assess his/her cognitive changes over weeks. The experimental results demonstrated individual nature of subjects' psychological and physiological changes over observation time (1,5 month) and their relationship. The authors' approach is based on the model describing formation and functioning of the “functional system of cognitive activity”. The question discussed is: what indices of a human performance indicators (behavioral, internal and/or external) could be useful for model construction? A human cognitive load can be under influence of different external and internal factors that can provoke his/her performance degradation. The results demonstrated the similar tendency for studied groups of subjects in previous studies: individual nature of changes of cognitive and physiological indices in day-to-day performance, as well as potentially significant influence of parameters of solar wind on them. As a result, such type of measurements of internal (physiological) and external (solar physics) parameters in combination with test performance indices could be used for assessment and prediction of effectiveness of cognitive activity, and adaptation of learning process according to the particularly learner's readiness to learn on a particular day and time. Different approaches and methods were proposed to take into account a human state, abilities, individual features to plan a human activity. Looking at learning as a type of activity in human-system integration, it is possible to consider today’s learner as an operator-researcher who acts in digital environment. At the same time, a human and tools of activity need mutual adaptation in complex systems. Psychophysiological model of learning and cognitive abilities development could be a basis for more effective design of learning achievements, organization and process, their quality, namely for adaptive learning on the base of accounting a learner's current cognitive state indices. Those results could be applied in design of adaptive learning systems, as it was made for industry in the previous developments of the authors. Principles of use of student's state indices in adaptive learning systems are proposed.
. Conclusions of the Council and of the Representatives of the Governments of the Member States, meeting within the Council of 21 November 2008 on preparing young people for the 21st century: an agenda for European cooperation on schools. Document 52008XG1213(05). [online]. Available:http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:52008XG1213, 2008 (in English).
. O. Pinchuk, S. Lyvynova, O. Burov. Synthetic educational environment – a footpace to new education”, In: Inf. Techn. and Lern. Tools. 60, # 4, 28—45, 2017(in Ukrainian).
. V. Iu. Bykov, O.M. Spirin, O.P. Pinchuk. General secondary education as the basic link in the system of continuous education, Naukove zabezpechennia rozvytku osvity v Ukraini: aktualni problemy teorii i praktyky (do 25-richchia NAPN Ukrainy) [Tekst] : zbirnyk naukovykh prats. Kyiv : Vydavnychyi dim "Sam", s.175-245, 2017 (in Ukrainian).
. S. Lytvynova, O. Burov. Methods, Forms and Safety of Learning in Corporate Social Networks, In: ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Proceedings of the 13th Int. Conf. on ICT in Education, Research and Industrial Applications. Kyiv, Ukraine, May 15-18, 406—413. [online]. Available: http://ceur-ws.org/Vol-1844/10000406.pdf, 2017 (in English).
. M. Cariker. AR/VR: How Immersive Learning Technology Is Bringing Education and Training Into the Future, [online]. Available:https://edtechtimes.com/2018/09/27/how-immersive-learning-technology-is-bringing-education-and-training-into-the-future/, 2018 (in English).
. O. P. Pinchuk. Psychological and pedagogical features of environment of students’ e-learning , In: Bogachkov, Yr. M. (e.d.) Organization of e-Learning Environment in Secondary Schools, pp. 37--51. Pedaghoghichna dumka, Kyiv, 2012 (in Ukrainian).
. O. Iu. Burov, V. V. Kamyshin, N. I. Polikhun, А. Т. Asherov. Technologies of network resources’ use for young people training for research activity: Monograph, O. Iu. Burov (Eds.), К.: TOV «Informatsiini Systemy», 416 p., 2012 (in Ukrainian).
. W. Karwowski. A Review of Human Factors Challenges of Complex Adaptive Systems: Discovering and Understanding Chaos in Human Performance, Human Factors, 54.6, 2012 (in English).
. Raja Parasuraman, Thomas B. Sheridan, Fellow, and Christopher D. Wickens. A Model for Types and Levels of Human Interaction with Automation, IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 30, No. 3, 286-297, May 2000 (in English).
. Raja Parasuraman. Adaptive automation matched to human mental workload, In: Hockey, G.R.J., Gaillard, A.W.K., Burov, O. (Eds.), Operator Functional State. The Assessment and Prediction of Human Performance Degradation in Complex Tasks, NATO Science Series, IOS Press, Amsterdam, pp. 177-193, 2003 (in English).
. L. J. M. Mulder, A. Van Roon, H. Veldman, K. Laumann, O. Burov, L. Quispel, P.J. Hoogeboom. How to use cardiovascular state changes in adaptive automation, In: Hockey, G.R.J., Gaillard, A.W.K., Burov, O. (Eds.), Operator Functional State. The Assessment and Prediction of Human Performance Degradation in Complex Tasks. NATO Science Series, IOS Press, Amsterdam, pp. 260–272, 2003 (in English).
. O. Ratynska. Mental capacity to work of senior pupils in different weather types. – Manuscript, Scientific thesis for the Degree of the Candidate of Biological Sciences in the speciality 03.00.13 – human and animal physiology. – Kyiv National University named after Taras Shevchenko, Kyiv, 2005 (in Ukrainian).
. S. N. Vadziuk, O. M. Ratynska. Information leaflet 66-2006 Ukraine. Method of optimizing the educational process in senior pupils of secondary schools on the basis of weather conditions; opubl. biul. № 1, 24.03.2006 (in Ukrainian).
. Yu. V. Buntury O. V. Kanyshcheva M. A. Vovk Y. V. Liutenko. Adaptive learning as one of the promising directions in the modern information training system, Systemy Obrobky Informatsii, Vypusk 2 (148), 155-162, 2017 (in Russian).
. P. H. Chuprakov. Assessment of adaptation of students to educational activity using new computer technologies, Scientific thesis for the Degree of the Candidate of Biological Sciences in the speciality 03.00.13 – human and animal physiology, 19.00.02 – psychophysiology, Arkhanhelsk, [online]. Available: https://vivaldi.nlr.ru/bd000129564/view#page= 2000 (in Russian).
. O. Burov. Life-Long Learning: Individual Abilities versus Environment and Means, In: Proceedings of the 12th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Vol-1614, pp. 608-619, 2016 (in English).
. Scott N. Romaniuk. Adaptive learning in the classroom and beyond. [online]. Available: https://edtechnology.co.uk/Article/adaptive-learning-in-the-classroom-and-beyond. Accessed on: April 27, 2018 (in English).
. M. W. Scerbo, F. G. Freeman, P. J. Mikulka: A brain-based system for adaptive automation. Theor. Issues Ergon. Sci. 4, 200—219, doi:10.1080/1463922021000020891, 2003 (in English)
. H. Saqer and R. Parasuraman: Individual performance markers and working memory predict supervisory control proficiency and effective use of adaptive automation, Int. J. Human Factors and Ergonomics, Vol. 3, No. 1, pp.15–31, 2014 (in English).
. Ahmed Nisar, et al.. Statistical modelling of networked human-automation performance using working memory capacity. Ergonomics 57.3, 295-318, 2014 (in English).
. O. Spirin, O. Burov. “Models and Applied Tools for Prediction of Student Ability to Effective Learning”, 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Vol-2104, pp. 404-411, 2018 (in English).
. O. Burov. Day-to-day monitoring of an operator’s functional state and fitness-for-work: a psychophysiological and engineering approach. Ergonomics and Psychology. CRC Press, 107—126, 2008 (in English).
. Dale Basye. Personalized vs. differentiated vs. individualized learning, ISTE 1/24/2018, [online]. Available: https://www.iste.org/explore/articleDetail?articleid=124, 2018 (in English).
. SEC's Anonymous FTP Server (Solar-Geophysical Data). [online]. Available: http://sec.noaa.gov/ftpmenu/lists/ace2.html (in English)
. T.M. Zubchenko, Yu.A. Naumenko, O.Yu. Burov. ICT for studying the dynamics of school abilities under the influence of external and internal factors, Kompiuter v shkole y seme, V. 1, 3—14, 2017 (in Ukrainian).
. O. Iu. Burov, V. V. Kamyshin, Assessment of giftedness: problems of quantitative measure, Navchannia i vykhovannia obdarovanoi dytyny. Teoriia ta praktyka, K. Instytut Obdarovanoi Dytyny APN Ukrainy, Vyp. 2, 5-9, 2009 (in Ukrainian).
. O. M. Spirin. Criteria and indicators of quality ICT training, Informatsiini tekhnolohii i zasoby navchannia. Informatsiini tekhnolohii i zasoby navchannia, #1 (33). [online]. Available: http://journal.iitta.gov.ua, 2013 (in Ukrainian)
Authors who publish in this journal agree to the following terms:
- Authors hold copyright immediately after publication of their works and retain publishing rights without any restrictions.
- The copyright commencement date complies the publication date of the issue, where the article is included in.
- Authors grant the journal a right of the first publication of the work under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others freely to read, download, copy and print submissions, search content and link to published articles, disseminate their full text and use them for any legitimate non-commercial purposes (i.e. educational or scientific) with the mandatory reference to the article’s authors and initial publication in this journal.
- Original published articles cannot be used by users (exept authors) for commercial purposes or distributed by third-party intermediary organizations for a fee.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) during the editorial process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see this journal’s registered deposit policy at Sherpa/Romeo directory).
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Post-print (post-refereeing manuscript version) and publisher's PDF-version self-archiving is allowed.
- Archiving the pre-print (pre-refereeing manuscript version) not allowed.