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Multi Agent Based High School Physics Network Course Automatic Generation System

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e-Learning, e-Education, and Online Training (eLEOT 2023)

Abstract

The construction of online courses is the key to the modernization of education. Due to the backwardness of the application means of the existing network course automatic generation system, the network course automatic generation takes a long time and the function of the network course is less perfect. In order to solve the problems such as long time and imperfect function of high school physical network course automatic generation, this paper puts forward the design and research of high school physical network course automatic generation system based on multi-agent. Take high school physics as the research subject, determine the principles that need to be followed in the automatic generation of online courses, build the automatic generation framework of online courses, design the database of online courses, construct and configure multi-agent according to the automatic generation requirements of online courses. The multi-agent value decomposition stage and the multi-agent communication mechanism design stage enable the multi-agent to have the corresponding function of automatically generating network courses, which can automatically generate the required high school physics network courses. The experimental data shows that after the application of the design system, the minimum time spent for automatic generation of online courses is 16 s, and the maximum value of functional perfection of online courses is 99%, which fully proves that the application performance of the design system is better.

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References

  1. Ali, M.S., Agalya, R., Priya, B., et al.: Reliable controller for nonlinear multiagent system with additive time varying delay and nonlinear actuator faults. Math. Methods Appl. Sci. 45(1), 561–574 (2022)

    Article  MathSciNet  Google Scholar 

  2. Liu, Q.: Pseudo-predictor feedback control for multiagent systems with both state and input delays. IEEE/CAA J. Automat. Sinica 8(11), 1827–1836 (2022)

    Article  MathSciNet  Google Scholar 

  3. Luo, Y., Wang, X., Cao, J.: Guaranteed-cost finite-time consensus of multi-agent systems via intermittent control. Math. Methods Appl. Sci. 45(2), 697–717 (2022)

    Article  MathSciNet  Google Scholar 

  4. Pang, K., Pang, K., Ma, L., et al.: Probability-guaranteed secure consensus control for time-varying stochastic multi-agent systems under mixed attacks. J. Franklin Inst. 359(6), 2541–2563 (2022)

    Article  MathSciNet  Google Scholar 

  5. Weng, T., Xie, Y., Chen, G., et al.: Load frequency control under false data inject attacks based on multi-agent system method in multi-area power systems. Int. J. Distrib. Sens. Netw. 18(4), 4610–4618 (2022)

    Article  Google Scholar 

  6. Li, Z., Zhao, J.: Adaptive consensus of non-strict feedback switched multi-agent systems with input saturations. IEEE/CAA J. Automat. Sinica 8(11), 1752–1761 (2022)

    Article  MathSciNet  Google Scholar 

  7. Sigmon, A.J., Bodek, M.J.: Use of an online social annotation platform to enhance a flipped organic chemistry course. J. Chem. Educ. 99(2), 538–545 (2022)

    Article  Google Scholar 

  8. Viennot, L.: Incomplete explanations in physics teaching: discussing the rainbow with student teachers. Eur. J. Phys. 42(5), 055705 (2021)

    Article  Google Scholar 

  9. O’Brien, D.J.: A guide for incorporating E-teaching of physics in a post-COVID world. Am. J. Phys. 89(4), 403–412 (2021)

    Article  Google Scholar 

  10. Sedova, N., Sedov, V., Bazhenov, R., et al.: Neural network classifier for automatic course-keeping based on fuzzy logic. J. Intell. Fuzzy Syst. 40(2), 1–12 (2021)

    Google Scholar 

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Correspondence to Haiquan Chi .

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Chi, H., Zhao, Z., Jiang, X. (2024). Multi Agent Based High School Physics Network Course Automatic Generation System. In: Gui, G., Li, Y., Lin, Y. (eds) e-Learning, e-Education, and Online Training. eLEOT 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 543. Springer, Cham. https://doi.org/10.1007/978-3-031-51465-4_13

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  • DOI: https://doi.org/10.1007/978-3-031-51465-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51464-7

  • Online ISBN: 978-3-031-51465-4

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