Source: http://csmos.ru/index.php?page=mnt-issue-2018-2-11
Timestamp: 2019-04-25 06:23:50+00:00

Document:
The rapid growth of the volume of poorly structured, duplicating information limits its operational search, analysis and access to it. In this situation, the learning process based on information and communication technologies is also difficult. To solve these problems and effectively organize the learning process, it is necessary to structure the knowledge to be learned. It is known that for representation and structuring of knowledge in systems of training there are a number of models. The paper presents a comparative analysis of the most common models of knowledge representation and provides justification for the choice of the main model for use in intelligent learning systems.
Быстрый рост объема плохо структурированной дублирующей информации ограничивает её оперативный поиск и анализ. В этой ситуации процесс обучения, основанный на информационных и коммуникационных технологиях, также затруднен. Чтобы решить эти проблемы и эффективно организовать учебный процесс, необходимо структурировать изучаемую информацию. Известно, что для представления и структурирования знаний в системах обучения существует ряд моделей. В статье представлен сравнительный анализ наиболее распространенных моделей представления знаний и обоснованность выбора основной модели для использования в интеллектуальных обучающих системах.
L Jain, X Wu. Advanced Information and Knowledge Processing, London, UK Springer, 2009.
Strogalov A.S. Computer training systems: some problems of their development. university training in the information society / A.Strogalov // M .: RGGU, 1998.- P. 68 -72.
Uskov V.L., Uskov A.V. Modern Technology-Based Education: Instructor's Profile// Advanced Technology for Learning, Vol. 4, No. 3, 2007, P. 118-125.
Shikhnabieva T., Beshenkov S. Intelligent System of Training and Control of Knowledge, Based on Adaptive Semantic Models //Smart Education and e-Learning 2016. - Springer International Publishing, 2016. - С. 595-603.
Kudinov V. A. Principles of construction and use of expert training systems in the course "Theoretical foundations of Informatics": Diss. ... kand.ped.sciences / V. A. Kudinov. - M., 2000.
Kudryavtsev V. B., Vashik K., Strogalov A. S., Aliseychik P. A., Peretrukhin V. V. computer learning system of automatic type / / problems of theoretical Cybernetics. Moscow: RSUH, 1996. 111 p.
Shikhnabieva T. Sh. On the representation and control of knowledge in automated learning systems / T.Sh. Shikhnabieva // Journal of Informatics and Education ", No 10, 2008.- P.55 - 59.
Stuart Russell, Peter Norvig. Artificial intelligence: a Modern approach/ Stuart Russell, Peter Norvig // Moscow: Williams, 2006. - 1407 p.
Rastrigin L. A., M. X], Adaptive learning with a student model. - Riga: zenatne, 1988. - 160 p.
Gavrilova T. A., Khoroshevsky V. F. knowledge Base of intellectual systems. SPb.: Peter, 2000.- 384 p.
Wenger Е. Artificial intelligence and tutoring systems. Computational approaches to the communication of knowledge / Los Altos: Morgan Kaufmann, 1987.
Intelligent systems / / Tom.8. vol. 1 - 4. M., 2004. 594 p.
Kaziakhmedov T. B. model of multilevel adaptive training in Informatics in secondary school // Pedagogical Informatics, 2008, № 2. C. 3-11.
Kaziahmedov T. B. Modular level adaptive learning computer science: Philosophical aspects // Materials of scientific-practical conference of doctoral students, graduate students and applicants. Nizhnevartovsk: publishing house of NSPI, 2006.- Pp. 49-51.
Kudryavtsev V. B., Vashik K., Strogalov A. S., Aliseychik P. A., Peretrukhin V. V. Automata-based modeling of the learning process // Discrete math. Vol. 8. No. 4. 1996.- P. 3-10.
Solomatin N. Mmm. Information semantic systems. Moscow: Higher school, 1989. - 179 p.
Kuznetsov I. P. Semantic representations. M.: Science, 1986.- 296 p.

References: V. 
 V. 
 V. 
 V. 
 V. 
 V. 
 V.