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Virtual reality human–robot interaction technology acceptance model for learning direct current and alternating current

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Abstract

In this study, we have developed a set of virtual reality (VR) human–robot interaction technology acceptance model for learning direct current and alternating current, aiming to use VR technology to immerse students in the generation, existence, and flow of electricity. We hope that using VR to transform abstract physical concepts into tangible objects will help students learn and comprehend abstract electrical concepts. The VR technology acceptance model was developed using the Unity 3D game kit to be accessed using the HTC Vive VR headset. The scene models, characters, and objects were created using Autodesk 3DS Max and Autodesk Maya, and the 2D graphics were processed in Adobe Photoshop. The results were evaluated using four metrics for our technology acceptance model. The four metrics include the content, design, interface and media content, and practical requirements. The average score of the content is 4.73. The average score of the design is 4.12. The average score of the interface and media content is 4.34. The average score of the practical requirements is 3.72. All the items on the effectiveness questionnaire of the technology acceptance model had average scores in the range 4.25–4.75. Therefore, all teachers were strongly satisfied with the trial teaching activity. The average score of each statement ranged within 3.58–4.03 for the satisfaction with the teaching material contents. Hence, the students were somewhat satisfied with this teaching activity. The average score of each statement ranged from 3.43 to 4.96 for the satisfaction with the implementation of the technology acceptance model. This result shows that the respondents were generally satisfied with the learning outcomes associated with these materials. The average score per question in this questionnaire was 3.92, and most of the questions have an average score greater than 3.8 for the feedback pertaining to satisfaction with the teaching material contents. In summary, a deeply immersive and interactive game was created using tactile somatosensory devices and VR that aim to utilize and enhance the fun and benefits associated with learning from games.

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References

  1. Liu D, Bhagat KK, Yuan G, Chang TW, Huang R (2017) The potentials and trends of virtual reality in education. In: Liu D, Dede C, Huang R, Richards J (eds) Virtual, augmented, and mixed realities in education. Smart computing and intelligence. Springer, Singapore, pp 105–130

    Google Scholar 

  2. Kavanagh S, Luxtonreilly A, Wuensche B, Plimmer B (2017) A systematic review of virtual reality in education. Themes in Sci Technol Educ 10(2):85–119

    Google Scholar 

  3. Makransky G, Lilleholt L (2018) A structural equation modeling investigation of the emotional value of immersive virtual reality human–robot interaction in education. Educ Tech Res Dev 66:1141–1164

    Article  Google Scholar 

  4. Huang K-T, Ball C, Francis J, Ratan R, Boumis J, Fordham J (2019) Augmented versus virtual reality human–robot interaction in education: an exploratory study examining science knowledge retention when using augmented reality/virtual reality human–robot interaction mobile applications. Cyberpsychol Behav Soc Netw 22(2):105–110

    Article  Google Scholar 

  5. Zhang X (2019) The college English teaching reform supported by multimedia teaching technology and immersive virtual reality technology. In: International Conference on Virtual Reality and Intelligent Systems (ICVRIS), pp 91–94

  6. Yin J, Ren H, Zhou Y (2021) The whole ship simulation training platform based on virtual reality human–robot interaction. IEEE Open J Intell Transp Syst 2:207–215

    Article  Google Scholar 

  7. Jang J, Ko Y, Shin WS, Han I (2021) Augmented reality and virtual reality human–robot interaction for learning: an examination using an extended technology acceptance model. IEEE Access 9:6798–6809

    Article  Google Scholar 

  8. Rho E, Chan K, Varoy EJ, Giacaman N (2020) An experiential learning approach to learning manual communication through a virtual reality human–robot interaction environment. IEEE Trans Learn Technol 13(3):477–490

    Article  Google Scholar 

  9. Pellas N, Dengel A, Christopoulos A (2020) A scoping review of immersive virtual reality human–robot interaction in STEM education. IEEE Trans Learn Technol 13(4):748–761

    Article  Google Scholar 

  10. Ding Y, Li Y, Cheng L (2020) Application of Internet of things and virtual reality technology in college physical education. IEEE Access 8:96065–96074

    Article  Google Scholar 

  11. Kang S, Kang S (2019) The study on the application of virtual reality in adapted physical education. Cluster Comput 22(1):2351–2355

    Article  Google Scholar 

  12. Zhang Q, Wang K, Zhou S (2020) Application and practice of VR virtual education platform in improving the quality and ability of college students. IEEE Access 8:162830–162837

    Article  Google Scholar 

  13. Pan X, Zheng M, Xu X, Campbell AG (2021) Knowing your student: targeted teaching decision support through asymmetric mixed reality collaborative learning. IEEE Access 9:164742–164751

    Article  Google Scholar 

  14. Yang CH, Liu S-F, Lin C-Y, Liu C-F (2020) Immersive virtual reality-based cardiopulmonary resuscitation interactive learning support system. IEEE Access 8:120870–120880

    Article  Google Scholar 

  15. Bohné T, Heine I, Gürerk Ö, Rieger C, Kemmer L, Cao LY (2021) Perception engineering learning with virtual reality. IEEE Trans Learn Technol 14(4):500–514

    Article  Google Scholar 

  16. Puggioni M, Frontoni E, Paolanti M, Pierdicca R (2021) ScoolAR: an educational platform to improve students’ learning through virtual reality. IEEE Access 9:21059–21070

    Article  Google Scholar 

  17. Wang H, Wang JH, Wang H, Chen C (2019) Factors influencing intention of facebook fans of companies to convert into actual buyers. J Database Manag 30:1–23

    Article  Google Scholar 

  18. Wang HY, Wang JH, Zhang J et al (2021) The collaborative interaction with Pokémon-Go robot uses augmented reality technology for increasing the intentions of patronizing hospitality. Inf Syst Front. https://doi.org/10.1007/s10796-021-10200-1

    Article  Google Scholar 

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Acknowledgements

This study was supported by the scientific research funds of Shandong University of Technology, Zibo, China. Jian-Hong Wang received his PhD degree in Communications Engineering from the National Chung Cheng University, Taiwan, in January 2015. He is currently a professor at School of Computer Science and Technology, Shandong University of Technology, Zibo, China. Jian-Hong Wang is the corresponding author of this paper and can be contacted at: wwwccucomtw@gmail.com or jhwang_2015@163.com.

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Authors and Affiliations

  1. Department of Digital Multimedia Design, National Taipei University of Business, Taipei City, 100, Taiwan

    Chih-Min Lo

  2. School of Computer Science and Technology, Shandong University of Technology, Zibo, China

    Jian-Hong Wang

  3. Department of Business Administration, National Changhua University of Education, Changhua, Taiwan

    Hsing-Wen Wang

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  1. Chih-Min Lo
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  2. Jian-Hong Wang
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  3. Hsing-Wen Wang
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Correspondence to Jian-Hong Wang.

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Lo, CM., Wang, JH. & Wang, HW. Virtual reality human–robot interaction technology acceptance model for learning direct current and alternating current. J Supercomput 78, 15314–15337 (2022). https://doi.org/10.1007/s11227-022-04455-x

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