Nothing Special   »   [go: up one dir, main page]

Skip to main content

Developing and Evaluating a Novel Gamified Virtual Learning Environment for ASL

  • Conference paper
  • First Online:
Human-Computer Interaction – INTERACT 2023 (INTERACT 2023)

Abstract

The use of sign language is a highly effective way of communicating with individuals who experience hearing loss. Despite extensive research, many learners find traditional methods of learning sign language, such as web-based question-answer methods, to be unengaging. This has led to the development of new techniques, such as the use of virtual reality (VR) and gamification, which have shown promising results. In this paper, we describe a gamified immersive American Sign Language (ASL) learning environment that uses the latest VR technology to gradually guide learners from numeric to alphabetic ASL. Our hypothesis is that such an environment would be more engaging than traditional web-based methods. An initial user study showed that our system scored highly in some aspects, especially the hedonic factor of novelty. However, there is room for improvement, particularly in the pragmatic factor of dependability. Overall, our findings suggest that the use of VR and gamification can significantly improve engagement in ASL learning.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /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 (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping 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. Bantupalli, K., Xie, Y.: American sign language recognition using deep learning and computer vision. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 4896–4899. IEEE (2018)

    Google Scholar 

  2. Battistoni, P., Di Gregorio, M., Sebillo, M., Vitiello, G.: AI at the edge for sign language learning support. In: 2019 IEEE International Conference on Humanized Computing and Communication (HCC), pp. 16–23. IEEE (2019)

    Google Scholar 

  3. Bheda, V., Radpour, D.: Using deep convolutional networks for gesture recognition in american sign language. arXiv preprint arXiv:1710.06836 (2017)

  4. Bialystok, E., et al.: Bilingualism in Development: Language, Literacy, and Cognition. Cambridge University Press, Cambridge (2001)

    Book  Google Scholar 

  5. Bradski, G., Kaehler, A.: OpenCV. Dr. Dobb’s J. Softw. Tools 3, 120 (2000)

    Google Scholar 

  6. Bragg, D., Caselli, N., Gallagher, J.W., Goldberg, M., Oka, C.J., Thies, W.: ASL sea battle: gamifying sign language data collection. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2021)

    Google Scholar 

  7. Camgoz, N.C., Koller, O., Hadfield, S., Bowden, R.: Sign language transformers: Joint end-to-end sign language recognition and translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10023–10033 (2020)

    Google Scholar 

  8. Dillon, J.V., et al.: TensorFlow distributions. arXiv preprint arXiv:1711.10604 (2017)

  9. Economou, D., Russi, M.G., Doumanis, I., Mentzelopoulos, M., Bouki, V., Ferguson, J.: Using serious games for learning British sign language combining video, enhanced interactivity, and VR technology. J. Univ. Comput. Sci. 26(8), 996–1016 (2020)

    Google Scholar 

  10. Goswami, Tilottama, Javaji, Shashidhar Reddy: CNN model for American sign language recognition. In: Kumar, Amit, Mozar, Stefan (eds.) ICCCE 2020. LNEE, vol. 698, pp. 55–61. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7961-5_6

    Chapter  Google Scholar 

  11. Jiang, X., Hu, B., Chandra Satapathy, S., Wang, S.H., Zhang, Y.D.: Fingerspelling identification for Chinese sign language via AlexNet-based transfer learning and Adam optimizer. Sci. Program. 2020, 1–13 (2020)

    Google Scholar 

  12. Kim, S., Ji, Y., Lee, K.B.: An effective sign language learning with object detection based ROI segmentation. In: 2018 Second IEEE International Conference on Robotic Computing (IRC), pp. 330–333. IEEE (2018)

    Google Scholar 

  13. Pallavi, P., Sarvamangala, D.: Recognition of sign language using deep neural network. Int. J. Adv. Res. Comput. Sci. 12, 92–97 (2021)

    Google Scholar 

  14. Python, Why: Python. Python Releases for Windows 24 (2021)

    Google Scholar 

  15. Samonte, M.J.C.: An assistive technology using FSL, speech recognition, gamification and online handwritten character recognition in learning statistics for students with hearing and speech impairment. In: Proceedings of the 2020 the 6th International Conference on Frontiers of Educational Technologies, pp. 92–97 (2020)

    Google Scholar 

  16. Schnepp, J., Wolfe, R., Brionez, G., Baowidan, S., Johnson, R., McDonald, J.: Human-centered design for a sign language learning application. In: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1–5 (2020)

    Google Scholar 

  17. Schrepp, M., Hinderks, A., Thomaschewski, J.: Applying the user experience questionnaire (UEQ) in different evaluation scenarios. In: Marcus, A. (ed.) DUXU 2014. LNCS, vol. 8517, pp. 383–392. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07668-3_37

    Chapter  Google Scholar 

  18. Schrepp, M., Thomaschewski, J., Hinderks, A.: Construction of a benchmark for the user experience questionnaire (UEQ). Int. J. Interact. Multimed. Artif. Intell. 4(4), 40–44 (2017)

    Google Scholar 

  19. Wang, J., Ivrissimtzis, I., Li, Z., Zhou, Y., Shi, L.: User-defined hand gesture interface to improve user experience of learning American sign language. In: Frasson, C., Mylonas, P., Troussas, C. (eds.) ITS 2023. LNCS, vol. 13891, pp. 479–490. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-32883-1_43

    Chapter  Google Scholar 

  20. Zhang, F., et al.: MediaPipe hands: on-device real-time hand tracking. arXiv preprint arXiv:2006.10214 (2020)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jindi Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Wang, J., Ivrissimtzis, I., Li, Z., Zhou, Y., Shi, L. (2023). Developing and Evaluating a Novel Gamified Virtual Learning Environment for ASL. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14142. Springer, Cham. https://doi.org/10.1007/978-3-031-42280-5_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-42280-5_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42279-9

  • Online ISBN: 978-3-031-42280-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics