SpringerLink
Log in

Applying a Recurrent Neural Network to Implement a Self-organizing Electronic Educational Course

  • Conference paper
  • First Online:
Data Analytics in System Engineering (CoMeSySo 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 910))

Included in the following conference series:

Abstract

This article discusses the features of the implementation of a self-organizing electronic educational course as an innovative form of learning. A recurrent neural network was used to implement the self-organizing e-course. A recurrent neural network has the ability to remember previous data and use it to make decisions in the current iteration. This feature allows to take into account the current level of student knowledge and based on it provide test questions. The practical part of the study presents the results of testing the self-organizing electronic course created using recurrent neural network on a group of students. The results confirm the increase of learning efficiency and improvement of students’ results #COMESYSO1120.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (Canada)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alyoussef, I.Y.: Acceptance of e-learning in higher education: the role of task-technology fit with the information systems success model. Heliyon 9(3), e13751 (2023). https://doi.org/10.1016/j.heliyon.2023.e13751

    Article  Google Scholar 

  2. Balogun, N.A., Adeleke, F.A., Abdulrahaman, M.D., Shehu, Y.I., Adedoyin, A.: Undergraduate students’ perception on e-learning systems during COVID-19 pandemic in Nigeria. Heliyon 9(3), e14549 (2023). https://doi.org/10.1016/j.heliyon.2023.e14549

    Article  Google Scholar 

  3. Behl, A., Jayawardena, N., Pereira, V., Islam, N., Del Giudice, M., Choudrie, J.: Gamification and e-learning for young learners: a systematic literature review, bibliometric analysis, and future research agenda. Technol. Forecast. Soc. Chang. 176, 121445 (2022). https://doi.org/10.1016/j.techfore.2021.121445

    Article  Google Scholar 

  4. Ouajdouni, A., Chafik, K., Boubker, O.: Measuring e-learning systems success: data from students of higher education institutions in Morocco. Data Brief 35, 106807 (2021). https://doi.org/10.1016/j.dib.2021.106807

    Article  Google Scholar 

  5. Chahal, J., Rani, N.: Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables. J. Comput. High. Educ. 34, 844–867 (2022)

    Article  Google Scholar 

  6. Hsu, H.-P., Guo, J.-L., Lin, F.-H., Chen, S.-F., Chuang, C.-P., Huang, C.-M.: Effect of involvement and motivation on self-learning: Evaluating a mobile e-learning program for nurses caring for women with gynecologic cancer. Nurse Educ. Pract. 67, 103558 (2023). https://doi.org/10.1016/j.nepr.2023.103558

    Article  Google Scholar 

  7. Sayaf, A.M.: Adoption of E-learning systems: an integration of ISSM and constructivism theories in higher education. Heliyon 9(2), e13014 (2023). https://doi.org/10.1016/j.heliyon.2023.e13014

    Article  Google Scholar 

  8. Tsarev, R., et al.: Improving test quality in e-learning systems. In: Silhavy, R., Silhavy, P. (eds.) Networks and Systems in Cybernetics. CSOC 2023. Lecture Notes in Networks and Systems, vol. 723, pp. 62–68. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35317-8_6

  9. Baranov, I.: Review and comparative analysis of BPMN-systems for robotization of business processes. Modern Innov. Syst. Technol. 2(3), 0139–0149 (2022). https://doi.org/10.47813/2782-2818-2022-2-3-0139-0149

  10. Bloomfield, J.G., Fisher, M., Davies, C., Randall, S., Gordon, C.J.: Registered nurses’ attitudes towards e-learning and technology in healthcare: a cross-sectional survey. Nurse Educ. Pract. 69, 103597 (2023). https://doi.org/10.1016/j.nepr.2023.103597

    Article  Google Scholar 

  11. Li, H., Lu, F., Hou, M., Cui, K., Darbandi, M.: Customer satisfaction with bank services: the role of cloud services, security, e-learning and service quality. Technol. Soc. 64, 101487 (2021). https://doi.org/10.1016/j.techsoc.2020.101487

    Article  Google Scholar 

  12. Obidova, Z.: Methodological issues of physical education in secondary school. Inform. Econ. Manag. 2(1), 0124–0131 (2023). https://doi.org/10.47813/2782-5280-2023-2-1-0124-0131

  13. Sattarkulov, K.R.: Methods of studying the law of absolute radiation of a black body in physics courses of academic lyceums. Inform. Econ. Manag. 2(1), 0132–0137 (2023). https://doi.org/10.47813/2782-5280-2023-2-1-0132-0137

  14. Solomon, J., Wayne, N., Cowell, L., Stenson, S., Hubbard, G.P.: The development and use of e-learning modules to support care home staff caring for enterally tube fed patients. Clin. Nutr. ESPEN 48, 520 (2022). https://doi.org/10.1016/j.clnesp.2022.02.103

    Article  Google Scholar 

  15. Alanis, V.M., Recker, W., Ospina, P.A., Heuwieser, W., Virkler, P.D.: Dairy farm worker milking equipment training with an E-learning system. JDS Commun. 3(5), 322–327 (2022). https://doi.org/10.3168/jdsc.2022-0217

    Article  Google Scholar 

  16. Singh, S., Hussain, S.Z.: Mechanising E-learning for equi** start-up entrepreneurs. Mater. Today Proc. 37(2), 2467–2469 (2021). https://doi.org/10.1016/j.matpr.2020.08.289

    Article  Google Scholar 

  17. Malik, S., Rana, A.: E-Learning: role, advantages, and disadvantages of its implementation in higher education. Int. J. Inf. Commun. Comput. Technol. 8(1), 403–408 (2020)

    Google Scholar 

  18. Tsarev, R., et al.: Gamification of the graph theory course. finding the shortest path by a greedy algorithm. In: Silhavy, R., Silhavy, P. (eds.) Networks and Systems in Cybernetics. CSOC 2023. Lecture Notes in Networks and Systems, vol. 723, pp. 209–216. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35317-8_18

  19. Lunev, D., Poletykin, S., Kudryavtsev, D.O.: Brain-computer interfaces: technology overview and modern solutions. Modern Innov. Syst. Technol. 2(3), 0117–0126 (2022). https://doi.org/10.47813/2782-2818-2022-2-3-0117-0126

  20. Fan, K., Liu, W., He, K., Wang, Z., Ou, S., Wu, Y.: Review: the application of artificial intelligence in distribution network engineering field. Inform. Econ. Manag. 2(1), 0210–0218 (2023). https://doi.org/10.47813/2782-5280-2023-2-1-0210-0218

  21. Ezaldeen, H., Bisoy, S.K., Misra, R., Alatrash, R.: Semantics aware intelligent framework for content-based e-learning recommendation. Nat. Lang. Process. J. 2023, 100008 (2023). https://doi.org/10.1016/j.nlp.2023.100008

    Article  Google Scholar 

  22. Deetjen-Ruiz, R., et al.: Applying ant colony optimisation when choosing an individual learning trajectory. In: Silhavy, R., Silhavy, P. (eds.) Networks and Systems in Cybernetics. CSOC 2023. Lecture Notes in Networks and Systems, vol. 723, pp. 587–594. Springer, Cham (2023).https://doi.org/10.1007/978-3-031-35317-8_53

  23. Vedavathi, N., Kumar, A.K.M.: E-learning course recommendation based on sentiment analysis using hybrid Elman similarity. Knowl.-Based Syst. 259, 110086 (2023). https://doi.org/10.1016/j.knosys.2022.110086

    Article  Google Scholar 

  24. Gruzenkin, D.V., et al.: Neural networks to solve modern artificial intelligence tasks. J. Phys. Conf. Ser. 1399(3), 033058 (2019). https://doi.org/10.1088/1742-6596/1399/3/033058

    Article  Google Scholar 

  25. Samojlov, A.S., Goloborodko, E.V., Klyuchnikov, M.S.: Big data, machine learning and precision forecasting in sport medicine. Healthcare Educ. Secur. 1(17), 7–17 (2019)

    Google Scholar 

  26. Semenenko, M.G., et al.: How to use neural network and web technologies in modeling complex technical systemsю. In: IOP Conference Series: Materials Science and Engineering, vol. 537, no. 3, p. 032095 (2019). https://doi.org/10.1088/1757-899X/537/3/032095

  27. Semenova, E.A., Tsepkova, S.M.: Neural networks as a financial instrument. Inform. Econ. Manag. 1(2), 0168–0175 (2022). https://doi.org/10.47813/2782-5280-2022-1-2-0168-0175

  28. Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)

    Article  Google Scholar 

  29. Li, L., **e, X., Gao, T., Wang, J.: A modified conjugate gradient-based Elman neural network. Cogn. Syst. Res. 68, 62–72 (2021). https://doi.org/10.1016/j.cogsys.2021.02.001

    Article  Google Scholar 

  30. Toha, S.F., Tokhi, M.O.: MLP and Elman recurrent neural network modelling for the TRMS. In: Proceedings of the 7th IEEE International Conference on Cybernetic Intelligent Systems, pp. 1–6. IEEE, London, UK (2008). https://doi.org/10.1109/UKRICIS.2008.4798969

  31. Zhang, Y., Wang, X., Tang, H.: An improved Elman neural network with piecewise weighted gradient for time series prediction. Neurocomputing 359, 199–208 (2019). https://doi.org/10.1016/j.neucom.2019.06.001

    Article  Google Scholar 

  32. Fan, Y., Yang, W: A backpropagation learning algorithm with graph regularization for feedforward neural networks. Inf. Sci. 607, 263−277 (2022)https://doi.org/10.1016/j.ins.2022.05.121

  33. Glushchenko, A., Petrov, V., Lastochkin, K.: Backpropagation method modification using Taylor series to improve accuracy of offline neural network training. Procedia Comput. Sci. 186, 202–209 (2021). https://doi.org/10.1016/j.procs.2021.04.139

    Article  Google Scholar 

  34. Kaveh, A., Servati, H.: Design of double layer grids using backpropagation neural networks. Comput. Struct. 79(17), 1561–1568 (2001). https://doi.org/10.1016/S0045-7949(01)00034-7

    Article  Google Scholar 

  35. Mandischer, M.: A comparison of evolution strategies and backpropagation for neural network training. Neurocomputing 42(1–4), 87–117 (2002). https://doi.org/10.1016/S0925-2312(01)00596-3

    Article  Google Scholar 

  36. Zaras, A., Passalis, N., Tefas, A.: Deep Learning for Robot Perception and Cognition, Chapter 2 - Neural networks and backpropagation. Eds Iosifidis, A., Tefas, A. Academic Press (2022). https://doi.org/10.1016/B978-0-32-385787-1.00007-5

Download references

Author information

Authors and Affiliations

  1. Tashkent State Transport University, Tashkent, Uzbekistan

    Ruslan Khakimzyanov

  2. University of Warwick, Coventry, UK

    Sadaquat Ali

  3. The Nukus branch of the Tashkent University of Information Technologies, Tashkent, Uzbekistan

    Bekbosin Kalmuratov

  4. University of Social Sciences and Humanities, Vietnam National University, Ho Chi Minh City (VNU-HCM), Vietnam

    Phuong Nguyen Hoang

  5. Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Andrey Karnaukhov

  6. MIREA - Russian Technological University (RTU MIREA), Moscow, Russia

    Roman Tsarev

  7. Bauman Moscow State Technical University, Moscow, Russia

    Roman Tsarev

Authors
  1. Ruslan Khakimzyanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadaquat Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bekbosin Kalmuratov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Phuong Nguyen Hoang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrey Karnaukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Roman Tsarev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roman Tsarev .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Petr Silhavy

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khakimzyanov, R., Ali, S., Kalmuratov, B., Hoang, P.N., Karnaukhov, A., Tsarev, R. (2024). Applying a Recurrent Neural Network to Implement a Self-organizing Electronic Educational Course. In: Silhavy, R., Silhavy, P. (eds) Data Analytics in System Engineering. CoMeSySo 2023. Lecture Notes in Networks and Systems, vol 910. Springer, Cham. https://doi.org/10.1007/978-3-031-53552-9_13

Download citation

Publish with us

Policies and ethics

Search

Navigation