Skip to main content

Advertisement

Log in

Modeling and simulation of virtual learning environment for automatic control principle

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

A Correction to this article was published on 18 January 2023

This article has been updated

Abstract

To improve students’ interest in learning, this paper proposes a Virtual Learning Environment (VLE) for the inverted pendulum control experiments in automatic control principle. The proposed VLE framework includes five levels: user interface, applications layer, models layer, platforms layer, and data layer, which facilitate the design, development, and implementation of the VLE system. And then, this paper constructs an intelligent Question-Answering (QA) model based on BERT, which can help the virtual tutor answer students’ questions about the inverted pendulum control input by text or voice. Moreover, in order to improve the interaction and intelligence of the virtual environment, this paper builds a virtual agent model to simulate human behavior. Finally, the VLE system is designed and implemented. Students can learn automatic control principle through the inverted pendulum experiments in the VLE system. By setting the parameters of the typical control algorithm simulated by MATLAB and observing the control result in the virtual reality environment, students can understand the control principle more effectively. According to the primary evaluation experiments, the immersive virtual learning environment can help students enhance their learning enthusiasm.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Change history

References

  1. Aithal SG, Rao AB, Singh S (2021) Automatic question-answer pairs generation and question similarity mechanism in question answering system. App Intel 51(11):8484–8497

    Article  Google Scholar 

  2. Cai LQ, Liu BB, Yu JM, Zhang JR (2017) Human behaviors modeling in multi-agent virtual environment. Multimed Tools Appl 76(4):5851–5871

    Article  Google Scholar 

  3. Cai LQ, Wei M, Zhou ST, Yan X (2020) Intelligent question answering in restricted domains using deep learning and question pair matching. IEEE Access 8:32922–32934

    Article  Google Scholar 

  4. Catelli R, Casola V, Pietro GD, Fujita H, Esposito M (2021) Combining contextualized word representation and sub-document level analysis through bi-Lstm+Crf architecture for clinical De-Identification. Knowledge-based syst 213(1):106649

    Article  Google Scholar 

  5. Chen D, Fisch A, Weston J, Bordes A (2017) Reading Wikipedia to answer open-domain questions. In: proceedings of the 55th annual meeting of the Association for Computational Linguistics (ACL 2017) 1:1870-1879

  6. Cheng XY, Wang YC, Chetan SS (2016) Using serious games in data communications and networking management course. J Comput Inf Syst 58(1):39–48

    Google Scholar 

  7. Dalgarno B, Gregory S, Knox V, Reiners T (2016) Practising teaching using virtual classroom role plays. Australian J Teach Educ 41(1):126–154

    Google Scholar 

  8. Devlin J, Chang MW, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. In: proceedings of the 2019 conference of the north American chapter of the Association for Computational Linguistics, pp 4171-4186

  9. Elfizar S, Fitriansyah A, Sastria G, Erna M (2020) Preserving Riau’s Malay culture through virtual environment application. IOP Conf Series Earth Environ Sci 519:012019

    Article  Google Scholar 

  10. Hui F, Wei T, Zheng E (2012) Research on inverted pendulum control based on LQR.LNEE 176.1:375-380

  11. Lemercier S, Thalmann D (2016) Multiple virtual human interactions. In: Context aware human-robot and human-agent interaction. Springer, Cham, pp 257–274. https://doi.org/10.1007/978-3-319-19947-4_12

  12. Li W, Allbeck JM (2020) Imperatives for virtual humans. arXiv preprint arXiv: 2004.10014. https://doi.org/10.48550/arXiv.2004.10014

  13. Liu X, Chen QC, Deng C, Zeng HJ, Chen J, Li DF, Tang BZ (2018) Lcqmc: a large-scale Chinese question matching corpus. In: Proceedings of the 27th international conference on computational linguistics, pp 1952–1962

  14. Meng YX, Wu W, Wang F, Li XY, Yin F, Li MY, Han QH, Sun XF, Li JW (2019) Glyce: glyph-vectors for Chinese character representations. Adv neural inform process syst 32(NIPS 2019):2742–2753

    Google Scholar 

  15. Merity S, Keskar NS, Socher R (2017) Regularizing and Optimizing Lstm Language Models. In: ICLR

  16. Mukhtar M, Daulay KR, Siregar E (2020) Design of a Problem-Based Virtual Learning Environment. J Phys Conf Ser 1462:012006

    Article  Google Scholar 

  17. Nissim Y, Weissblueth E (2017) Virtual reality (Vr) as a source for self-efficacy in teacher training. Int Educ Stud 10(8):52

    Article  Google Scholar 

  18. Noraset T, Lowphansirikul L, Suppawong T (2020) WabiQA: a Wikipedia-based Thai question-answering system. Inf Process Manag 58(1):102431

    Article  Google Scholar 

  19. Peng HH, Lin YT, Wu TL (2019) The effects of virtual learning environment on high school students’ english learning performance and attitude. In: International conference on innovative technologies and learning (ICITL). Springer, Cham, pp 815–824

  20. Quintana MGB, Fernández SM (2015) A pedagogical model to develop teaching skills. The collaborative learning experience in the immersive virtual world Tymmi. Comput Hum Behav 51:594–603

    Article  Google Scholar 

  21. Randhavane T, Bera A, Kapsaskis K, Gray K, Manocha D (2019) FVA: modeling perceived friendliness of virtual agents using movement characteristics. IEEE trans vis comp grap 25(11):3135–3145

    Article  Google Scholar 

  22. Reader J, Savin-Baden M (2020) Ethical conundrums and virtual humans. Postdigital Sci Educ 2(10):289–301

    Article  Google Scholar 

  23. Sait M, Alattas A, Omar A, Almalki S, Sharf S, Alsaggaf E (2019) Employing virtual reality techniques in environment adaptation for autistic children. Procedia Comp Sci 163:338–344

    Article  Google Scholar 

  24. Sasinka C, Stachon Z, Sedlak M, Chmelik J, Herman L, Kubicek P, Sasinkova A, Dolezal M, Tejkl H, Urbanek T, Svatonova H, Ugwitz P, Jurik V (2018) Collaborative immersive virtual environments for education in geography. ISPRS Int J Geo Inf 8(1):3

    Article  Google Scholar 

  25. Shao XQ, Feng XH, Yu YL, Wu ZH, Wei P (2020) A natural interaction method of multi-sensory channels for virtual assembly system of power transformer control cabinet. IEEE Access 8:54699–54709

    Article  Google Scholar 

  26. Shin S, Jin X, Jung J, Lee KH (2019) Predicate constraints based question answering over knowledge graph. Inf Process Manag 56(3):445–462

    Article  Google Scholar 

  27. Stocker C, Sun L, Huang P, Qin W, Allbeck JM, Badler NI (2010) Smart events and primed agents. In the proceedings of 10th international conference of the intelligent virtual agents 15-27

  28. Theelen H, Van D, Brok PD (2018) Classroom simulations in teacher education to support preservice teachers' interpersonal competence: a systematic literature review. Comput Educ 129:14–26

    Article  Google Scholar 

  29. Wang Z, Zhang X, Tan Y (2021) Chinese sentences similarity via cross-attention based siamese network. arXiv preprint arXiv: 2104.08787

  30. Xie R, Lu Y, Lin F et al (2020) FAQ-based question answering via knowledge anchors. In: CCF international conference on natural language processing and Chinese computing. Springer, Cham, pp 3–15

  31. Zhao Z (2019) Engaging audiences in virtual museums by interactively prompting guiding questions. arXiv preprint arXiv: 1902.03527

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shizhou Cao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: The image of Figure 2 in the original publication of this article was incorrect.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, L., Cao, S., Yi, W. et al. Modeling and simulation of virtual learning environment for automatic control principle. Multimed Tools Appl 81, 43679–43699 (2022). https://doi.org/10.1007/s11042-022-13099-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13099-1

Keywords

Navigation