Применение методов нечеткой логики для формирования адаптивной индивидуальной траектории обучения на основе динамического управления сложностью курса
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Use of fuzzy logic to create an adaptive individual learning path based on dynamic course complexity management

idBelov M.A. idGrishko S.I. idZhivetyev A.V. idPodgorny S.A. idTokareva N.A.

UDC 510.676, 519.7
DOI: 10.26102/2310-6018/2022.39.4.018

  • Abstract
  • List of references
  • About authors

The article presents a model for creating an adaptive individual learning path based on dynamic control of the course complexity using fuzzy logic methods. The model helps to individually manage the complexity of the training course for each student and to formalize the process of solving practical tasks and feedback from students motivating them to study productively taking into account personal characteristics and preferences. Implementation of such models and corresponding systems in the training process enables teachers to choose the most appropriate tasks for each student with due regard for individual characteristics and personality. The approach allows teachers to allocate more time for scientific, methodological and creative work, especially with the option to distribute educational materials in the form of microlearning, where a large number of students, usually studying online, is invited to perform many small practical tasks. Also, adaptive learning paths are designed to promote the development of adaptive thinking and adaptive strategies for students behavior. The individual learning path is an important element of online learning management system in the cloud environment of "Virtual Computer Lab" educational data center created by M.A. Belov (https://belov.global) in 2007 at the Institute of System Analysis and Control of Dubna State University, the hallmark of which are the principles of self-organization.

1. 1. Grishko S., Belov M., Cheremisina E., Sychev P. Model for creating an adaptive individual learning path for training digital transformation professionals and big data engineers using Virtual Computer Lab. Communications in Computer and Information Science. 2021;1448:496–507. DOI: 10.1007/978-3-030-87034-8_36.

2. 2. Belov M.A., Grishko S.I., Lishilin M.V., Osipov PA, Cheremisina E.N. Strategy for training IT specialists using the innovative training data center "virtual computer laboratory" to effectively solve the problems of digital transformation and acceleration of the digital economy. Sovremennye informatsionnye tekhnologii i IT-obrazovanie = Modern information technology and IT education. 2021;17(1):134–144. DOI: 10.25559/SITITO.17.202101.703. (In Russ.).

3. 3. Belov M.A., Grishko S.I., Cheremisina E.N., Tokareva N.A. Training IT specialists in a global digital transformation. The concept of automated management of competency profiles in educational programs of the future. Sovremennye informatsionnye tekhnologii i IT-obrazovanie = Modern information technology and IT education. 2021;17(3):658–669. DOI: 10.25559/SITITO.17.202103.658-669. (In Russ.).

4. 4. Cheremisina E.N., Belov M.A., Tokareva N.A., Nabiullin A.K., Grishko S.I., Sorokin A.V. Embedding of containerization technology in the core of the virtual computing lab. CEUR Workshop Proceedings. 26. Сер. "Selected Papers of the 26th International Symposium on Nuclear Electronics and Computing, NEC 2017". 2017;2023:299–302.

5. 5. Belov M., Grishko S., Cheremisina E., Tokareva N. Concept of peer-to-peer caching database for transaction history storage as an alternative to blockchain in digital economy. CEUR Workshop Proceedings. 9. Сер. "GRID 2021 – Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". 2021;3041:494–497.

6. 6. Cheremisina E., Tokareva N., Kirpicheva E., Kreider O., Milovidova A., Potemkina S. The concept of training IT professionals in the cross-cutting digital technologies. CEUR Workshop Proceedings. 9. Сер. "GRID 2021 – Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". 2021;3041:525–529.

7. 7. Milovidova A.A., Cheremisina E.N., Dobrynin V.N. Algorithm for determining the type and parameters of the function of belonging to an odd meter. Sovremennaya nauka: aktual'nye problemy teorii i praktiki. Seriya: Estestvennye i tekhnicheskie nauki = Modern science: current problems of theory and practice. Series: Natural and Technical Sciences. 2019;9:69–74. (In Russ.).

8. 8. Dobrynin V.N., Milovidova A.A., Sokolov I.A. Assessment of the adequacy of the model and the study object. Information and telecommunication technologies and mathematical modeling of high-tech systems. Materials of the All-Russian Conference with international participation. Moscow, RUDN Publishing House; 2018:252–256. (In Russ.).

9. 9. Mitroshin P.A. Automation of the process of measuring the level of development of competencies in a competence-oriented training model. Metrological support of innovative technologies. International Forum. St. Petersburgh, St. Petersburg State University of Aerospace Instrumentation; 2020:204–205. (In Russ.).

10. 10. Mitroshin P.A. Models and algorithms to support the management of the learning process. In the collection: Informatization of education and the methodology of e-learning: digital technologies in education. Proceedings of the IV International Scientific Conference. Krasnoyarsk, V.P. Astafiev Krasnoyarsk State Pedagogical University; 2020:263–268. (In Russ.).

11. 11. Belov M.A., Antipov O.E. Control and measurement system for assessing the quality of training in a virtual computer laboratory, quality. Nauka. Innovatsii. Obrazovanie = Science. Innovation. Education. 2012;3:28–37. (In Russ.).

12. 12. Cheremisina E.N., Belov M.A., Antipov O.E., Sorokin A.V. Innovative practice of computer education at Dubna University using a virtual computer laboratory based on cloud computing technology. Programmnaya inzheneriya = Software engineering. 2012;5:34–41. (In Russ.).

13. 13. Cheremisina E.N., Kramarov N.L., Belov M.A. Practical system analysis. Building models of concepts in projects to improve the efficiency of organizations. A textbook for students of higher education institutions. Dubna, State University "Dubna"; 2012. 149 p. (In Russ.).

14. 14. Cheremisina E.N., Belov M.A., Lishilin M.V. Integration of a virtual computer laboratory and a knowledge space is a new look at the training of highly qualified IT specialists. Sistemnyi analiz v nauke i obrazovanii = Systems analysis in science and education. 2014;1(23):97–104. (In Russ.).

15. 15. Sidorov D.S., Belov M.A. Design of hardware and software systems in the educational process with the use of a virtual computer laboratory. Sistemnyi analiz v nauke i obrazovanii = Systems analysis in science and education. 2020;2:70–82. (In Russ.).

Belov Mikhail Aleksandrovich
Candidate of Technical Sciences

WoS | Scopus | ORCID | eLibrary |

Dubna State University

Dubna, Russian Federation

Grishko Stanislav Ivanovich

ORCID |

Dubna State University

Dubna, Russian Federation

Zhivetyev Aleksandr Viktorovich

ORCID |

Dubna State University

Dubna, Russian Federation

Podgorny Sergey Aleksandrovich
Doctor of Technical Sciences, Professor

ORCID |

Dubna State University

Dubna, Russian Federation

Tokareva Nadezhda Aleksandrovna
Candidate of Physical and Mathematical Sciences, Associate Professor

ORCID |

Dubna State University

Dubna, Russian Federation

Keywords: IT education methodology, distance learning, individual learning path, digital transformation, microlearning, fuzzy logic, virtual computer lab

For citation: Belov M.A. Grishko S.I. Zhivetyev A.V. Podgorny S.A. Tokareva N.A. Use of fuzzy logic to create an adaptive individual learning path based on dynamic course complexity management. Modeling, Optimization and Information Technology. 2022;10(4). Available from: https://moitvivt.ru/ru/journal/pdf?id=1249 DOI: 10.26102/2310-6018/2022.39.4.018 (In Russ).

246

Full text in PDF

Received 24.11.2022

Revised 03.12.2022

Accepted 27.12.2022

Published 30.12.2022