Research on the Design of Modern Distance Education System Based on Agent Technology

Le Xiu

Authors

Le Xiu University of California, Los Angeles, California, 90024, United States Author

Keywords:

agent technology, distance education system, personalized recommendation, intelligent teaching, task allocation algorithm

Abstract

With the rapid development of information technology, modern distance education systems have gradually become an important part of the education field. However, traditional distance education systems face several shortcomings in personalized learning, real-time interaction, and intelligent teaching. This paper proposes a modern distance education system design based on Agent technology, aiming to achieve intelligent management of learning resources, personalized teaching recommendations, and real-time monitoring of teacher-student interaction through the autonomy, collaboration, and adaptability of intelligent Agents. The paper first analyzes the current needs of distance education systems, then designs an Agent-based system architecture, and describes the core algorithms of task allocation, intelligent recommendation, and collaboration. Finally, the system's performance was tested and evaluated, suggesting potential improvements in learning outcomes and user experience. This research provides new insights and technical support for the development of modern distance education systems.

References

1. R. Li, "An artificial intelligence agent technology based web distance education system," J. Intell. Fuzzy Syst., vol. 40, no. 2, pp. 3289–3299, 2021, doi: 10.3233/JIFS-189369.

2. M. Garlinska, K. Musial, J. Kurek, J. Marcinkiewicz, P. Domanski, and A. Puczynski, et al., "The influence of emerging tech-nologies on distance education," Electronics, vol. 12, no. 7, p. 1550, 2023, doi: 10.3390/electronics12071550.

3. S. Li and W. Han, "Design of distance education system for ideological and political courses of electronic engineering specialty based on big data," J. Interconnect. Netw., vol. 22, suppl. 05, p. 2147009, 2022, doi: 10.1142/S0219265921470095.

4. S. Xie and J. Xu, "Design and implementation of physical education teaching management system based on multi-agent model," Int. J. Comput. Intell. Syst., vol. 16, no. 1, p. 172, 2023, doi: 10.1007/s44196-023-00349-9.

5. Y. Huang, "Design of personalised English distance teaching platform based on artificial intelligence," J. Inf. Knowl. Manag., vol. 21, suppl. 02, p. 2240017, 2022, doi: 10.1142/S0219649222400172.

6. O. Barabash, A. Shestakov, L. Barabash, D. Yankovoy, I. Konovalova, and S. Ivanova, et al., "Comprehensive methods of evaluation of efficiency of distance learning system functioning," Int. J. Comput. Netw. Inf. Secur., vol. 14, no. 1, pp. 16–23, 2021, doi: 10.5815/ijcnis.2021.01.02.

7. O. Barabash, A. Shestakov, L. Barabash, D. Yankovoy, I. Konovalova, and S. Ivanova, et al., "Comprehensive methods of evaluation of distance learning system functioning," Int. J. Comput. Netw. Inf. Secur., vol. 13, no. 3, pp. 62–71, 2021, doi: 10.5815/ijcnis.2021.03.06.

8. S. de O. Durso and E. P. Arruda, "Artificial intelligence in distance education: A systematic literature review of Brazilian studies," Probl. Educ. 21st Century, vol. 80, no. 5, pp. 679–692, 2022, doi: 10.33225/pec/22.80.679.

9. A. M. M. Mahmoud, "An agent-based framework for providing security in a cloud-based E-learning system," Int. J. Adv. Appl. Sci., vol. 7, pp. 19–24, 2020, doi: 10.21833/ijaas.2020.07.003.

10. Y. Liu and M. Shabaz, "Design and research of computer network micro-course management system based on JSP technology," Int. J. Syst. Assur. Eng. Manag., vol. 13, suppl. 1, pp. 203–211, 2022, doi: 10.1007/s13198-021-01368-5