OWL model of multi-agent Smart-system of distance learning for people with vision disabilities

The aim of the study is to develop an ontological model of multiagent smart-system of distance learning for visually impaired people based on Java Agent Development Framework for obtaining high-quality engineering education in laboratories of join use on modern equipment.

Materials and methods of research. In developing multi-agent smart-system of distance learning, using various agents based on cognitive, ontological, statistical and intellectual methods is important. It is more convenient to implement this task in the form of software using multi-agent approach and Java Agent Development Framework. The main advantages of the platform are stability of operation, clear interface, simplicity of creating agents and extensive user database. In multi-agent systems, the solution is obtained automatically as result of interaction of many independent, purposeful agents. Each agent can perform certain tasks and pursue specified goals. Intellectual multi-agent systems and practical applications in distance learning based on them are considered.

Results. The structural diagram of functioning of smart system distance learning for visually impaired people using various agents based on the system approach and the multi-agent platform Java Agent Development Framework is developed. The complex approach of distance learning of visually impaired people for obtaining highquality engineering education in laboratories of joint use on modern equipment is offered.

The ontological model of multi-agent smart-system with a detailed description of the functions of following agents is created: personal, manager, ontological, cognitive, statistical, intellectual, shared laboratory agent, health agent, assistant to the agent and state agent. These agents execute their individual functions and provide a quality environment for learning.

Conclusion. Thus, the proposed smart-system of distance learning for visually impaired people can significantly improve effectiveness and quality of the received education of this category of people. The benefits of using of the developed ontological model of smartsystem of distance learning for visually impaired people based on multifunctional agents are: complex approach, based on the use of various intellectual, cognitive and statistical methods; possibility of developing an individual trajectory of learning for visually impaired people including the psychophysiological features of perception information; distance access to the latest technological equipment for performing laboratory and practical works by visually impaired people in the shared laboratories in real time. The ontological model provides to analyze more deeply the numerous connections between agents and considers it in developing software for smart-system of distance learning for visually impaired people. Multi-agent approach provides multi functionality of system, stability to system errors, and optimization of computing resources. 

1. Olaf Zawacki-Richter Distance Education // Encyclopedia of Educational Philosophy and Theory. Springer Science, 2015, P. 1–7.

2. Tikhomirov V., Dneprovskaya N., Yankovskaya E. Three Dimensions of Smart Education // Smart Education and Smart e-Learning. Switzerland: Springer International Publishing, 2015. Vol. 41. P. 47–56.

3. Vytautas Štuikys. Smart Education in CS: A Case Study // Smart Learning Objects for Smart Education in Computer Science. Switzerland: Springer International Publishing, 2015. P. 287–310.

4. Samigulina G., Samigulina Z. Intelligent System of Distance Education of Engineers, based on Modern innovative Technologies // Proce. 2nd Intern. Conf. Higher Education Advances, Valencia, Spain, Elsevier, 2016. Vol. 228. P. 229–236.

5. Bakken J., Uskov V., Penumatsa A., Doddapaneni A. Smart Universities, Smart Classrooms and Students with Disabilities // Smart Education and Smart e-Learning. Switzerland: Springer International Publishing, 2016. Vol. 59. P. 15–27.

6. Nikashina N. Particular of development people with confined eyesight // Vector of science. 2012. Vol. 2. P. 223–226.

7. Samigulina G., Shayakhmetova A. The information system of distance learning for people with impaired vision on the basis of artificial intelligence approaches // Proc. 2nd Intern. Conf. Smart Education and E-learning. Italy: Springer, 2015. P. 255–265.

8. Stamatis P., Panagiotopoulos I., Goumopoulos C., Kameas A. APLe: Agents for Personalized Learning in Distance Learning // Computer Supported Education. Switzerland: Springer International Publishing, 2016. Vol. 583. P. 37–56.

9. Punnarumol Temdee. Ubiquitous Learning Environment: Smart Learning Platform with MultiAgent Architecture // Wireless Personal Communications. US:Springer International Publishing, 2014. Vol. 76. P. 627–641.

10. Roger A., Jason H., Gregory T., Melissa D., Reza F., François B., Ronald L. Using Trace Data to Examine the Complex Roles of Cognitive, Metacognitive, and Emotional Self-Regulatory Processes During Learning with Multi-agent Systems // Handbook. Metacognition and Learning Technologies. New York: Springer Science, 2013.

11. Samigulina G., Shayakhmetova A. Development of the Smart – system of distance learning visually impaired people on the basis of the combined OWL model // Proc. 3rd Intern. Conf. Smart Education and E-learning. Spain: Springer, 2016. P. 109–118.

12. Primo T., Andrе B., Rosa M. Vicari. A Semantic Web Approach to Recommend Learning Objects // Highlights on Practical Applications of Agents and Multi-Agent Systems. Berlin: Springer, 2013. Vol. 365. P. 340–350.

13. Paula R., Nеstor D., Demetrio A. Ovalle Multi-agent System for Knowledge-Based Recommendation of Learning Objects Using Metadata Clustering // Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability. Switzerland: Springer International Publishing, 2015. Vol. 524. P. 356–364.

14. Keleberda I., Lesnaya N., Repka V. Ispolzovanie multiagentnogo ontologicheskogo podhoda k sozdaniyu raspredelennih system // Educational Technology & Society. 2004. Vol. 7. P. 190–205.

15. Lo W. A. Value-Adding Process Using the Ontological Engineering Approach for an e-Learning System Design // Proc. 3rd Intern. Conf. Singapore. 2011. P. 412–416.

16. Rong S. Application of Multi-agent in Personalized Education System // Advances in Computer Science, Environment, Ecoinformatics, and Education. Berlin: Springer, 2011. Vol. 5. P. 518–522.

17. Usef F., Philippe F., Roger N. A. Cognitive Tutoring Agent with Human-Like Learning Capabilities and Emotions // Intelligent and Adaptive Educational-Learning Systems. Berlin: Springer, 2013. Vol. 17. P 339–365.

18. Chatzarа K., Karagiannidis C., Stamatis D. Cognitive support embedded in self-regulated elearning systems for students with special learning needs // Education and Information Technologies. New York: Springer Science, 2014. P. 283–299.

19. Shuhua H., Xuemei S. The Study on University Library Information Service for Regional Characterized Economy Construction Based on Integrated Agent. Computer Supported Education. Berlin: Springer, 2013. P. 289–294.

20. JADE Architecture Overview. http://www. jade.tilab.com/ : 3.06.2017.