doi: 10.17586/2226-1494-2018-18-3-543-553


CREATION OF INDIVIDUAL LEARNING TRAJECTORIES BASED ON STUDENT’S ACHIEVEMENTS AND FUNCTIONAL STATE ANALYSIS

A. V. Lyamin


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Article in Russian

For citation: Lyamin A.V. Creation of individual learning trajectories based on student’s achievements and functional state analysis. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 3, pp. 543–553 (in Russian). doi: 10.17586/2226-1494-2018-18-3-543-553

Abstract
 Subject of Study. Modern standards specifying competencies and learning outcomes have been analyzed. The model of an educational program has been developed providing building a correct educational program based on the prerequisites and planned outcomes analysis. A creation concept has been specified for a learning management system of new generation that allows forming individual learning trajectories based on analysis of student’s achievements, wishes and peculiarities. Method. The rules of creating a correct educational program were defined, which, together with prerequisites, planned outcomes, achievements, wishes and functional state analysis, lead to automatic individual trajectory analysis. Main Results. The analysis of modern educational standards has been performed; models and rules of forming educational module sets used for learning trajectory creation have been developed; models that analyze students' achievements during the learning trajectory formation have been developed; method of student’s functional state analysis has been developed aimed at creating adaptive educational environment. Practical Relevance. Forming individual learning trajectories based on student’s achievements and functional state analysis provides the possibility to build educational process automatically that will be suitable for a specific student and satisfy the requirements of the educational program.

Keywords: individual learning trajectories, information systems, e-learning, learning management systems, functional state

References
 1.      Hagedorn C., Meinel C. Exploring the potential of game-based learning in massive open online courses // Proc. IEEE 17th Int. Conf. on Advanced Learning Technologies (ICALT). Timisoara, Romania, 2017. P. 542–544. doi: 10.1109/ICALT.2017.119
2.      Du Z., Chen H., Jiang J. Research on the big data system of massive open online course // Proc. IEEE Int. Conf. on Big Data. Washington, 2016. P. 1931–1936. doi: 10.1109/BigData.2016.7840813
3.      Zang X., Iqbal S., Zhu Y., Riaz M.S., Abbas G., Zhao J. Are MOOCs advancing as predicted by IEEE CS 2022 report? // Proc. 2nd Int. Conf. on Proceedings of the Systems Informatics, Modelling and Simulation (SIMS). Riga, Latvia, 2016.
P. 49–55. doi: 10.1109/SIMS.2016.14
4.      Лямин А.В., Чежин М.С. Построение электронных курсов для открытого онлайн-обучения // Труды XX Всероссийской научно-методической конференции «Телематика'2013». 2013. С. 165–166.
5.      Lisitsyna L.S., Lyamin A.V., Martynikhin I.A., Cherepovskaya E.N. Cognitive trainings can improve intercommunication with e-Learning system // Proc. 6th Int. Conf. Series on Cognitive Infocommunications. Gyor, Hungary, 2015. P. 39–44. doi: 10.1109/CogInfoCom.2015.7390561
6.      Лямин А.В., Васильев В.Н., Колесников Ю.Л., Чежин М.С. Опыт использования компьютерных образовательных технологий в национальном исследовательском университете информационных технологий, механики и оптики // Материалы международной научно-практической конференции «Дистанционные технологии в образовании – 2011». 2011. С. 68–70.
7.      Лямин А.В., Чежин М.С. Развитие электронного обучения, дистанционных образовательных технологий в НИУ ИТМО // Информационная среда вуза XXI века: материалы VII Всероссийской научно-практической конференции. 2013. С. 145–148.
8.      Moritz D., Willems C., Goderbauer M., Moeller P., Meinel C. Enhancing a virtual security lab with a private cloud framework // Proc. IEEE Int. Conf. on Teaching, Assessment and Learning for Engineering (TALE). Kuta, Indonesia, 2013. P. 314–320. doi: 10.1109/TALE.2013.6654452
9.      Hristov G., Zahariev P., Bencheva N., Ivanov I. Designing the next generation of virtual learning environments - Virtual laboratory with remote access to real telecommunication devices // Proc. 24th EAEEIE. Chania, Greece, 2013.
P. 139–144. doi: 10.1109/EAEEIE.2013.6576517
10.   Bistak P., Huba M. Three-tank virtual laboratory for dynamical feedforward control based on Matlab // Proc. 19th Int. Conf. on Electrical Drives and Power Electronics. Dubrovnik, Croatia, 2017. P. 318–323. doi: 10.1109/EDPE.2017.8123223
11.   Lyamin A.V., Cherepovskaya E.N., Chezhin M.S. An outcome-based framework for developing learning trajectories // Smart Innovation, Systems and Technologies. 2017. V. 75.
P. 129–142. doi: 10.1007/978-3-319-59451-4_14
12.   Gritschneder F., Hatzelmann P., Thom M., Kunz F., Dietmayer K. Adaptive learning based on guided exploration for decision making at roundabouts // Proc. IEEE Intelligent Vehicles Symposium. Gotenburg, Sweden, 2016. P. 433–440. doi: 10.1109/IVS.2016.7535422
13.   Ефимчик Е.А., Лямин А.В. Автоматизация подготовки вариантов и оценивания решений алгоритмических заданий для виртуальных лабораторий на основе автоматной модели // Дистанционное и виртуальное обучение. 2015. № 6(96). С. 20–33.
14.   Yokozuka T., Thepsoonthorn C., Miura S., Yap R.M.S., Kwon J., Ogawa K., Miyake Y. Body and psychological state synchrony and change by the grant of prior knowledge // Proc. IEEE/SICE International Symposium on System Integration. Nagoya, Japan, 2015. P. 906–911. doi: 10.1109/SII.2015.7405133
15.   InLOC Standard: Integrating Learning Outcomes and Competences. 2013.
16.   ISO/IЕC 20006-1. Information Technology for Learning, Education and Training: Information Model for Competency, Part 1: Competency General Framework and Information Model. 2014.
17.   ISO/IЕC 20006-2. Information Technology for Learning, Education and Training: Information Model for Competency, Part 2: Proficiency Level Information Model. 2014.
18.   IEEE Std 1484.20.1-2007. IEEE Standard for Learning Technology: Data Model for Reusable Competency Definitions.
19.   Романовский И.В. Дискретный анализ. Изд. 4-е. СПб.: Невский Диалект; БХВ-Петербург, 2008. 335 с.
20.   Падерно П.И., Попечителев Е.П. Надежность и эргономика биотехнических систем. СПб.: СПбГЭТУ, 2007. 288 с.
21.   Егоров А.С., Загрядский В.П. Психофизиология умственного труда. Л.: Наука, 1973.
Лямин А.В., Скшидлевский А.А. Программное обеспечение для выявления влияния обучающего воздействия на функциональное состояние студента // Труды XVII Всероссийской научно-методической конференции «Телематика'2010». 2010. Т. 1. С. 188–189


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