Abstract
Body-worn sensors can be used to capture, analyze, and replay human performance for training purposes. The key challenge to any such approach is to establish validity that the captured expert experience is actually suitable for training. In this paper, to evaluate this, we apply a questionnaire-based expert assessment and a complementary trainee knowledge assessment to study the approach adopted and the models generated with the WEKIT solution, a hardware and software application that complements Augmented Reality glasses with wearable sensor-actuator experience. This solution was developed using the ID4AR framework which as also developed within the WEKIT project. ID4AR framework is a domain agnostic framework which can be used to design augmented reality and sensor based applications for training. The study presented triangulates validity across three independent test-beds in the professional domains of aircraft maintenance, medical imaging, and astronaut training, with 61 experts completing the expert survey and 337 students completing the trainee knowledge test. Results show that the captured expert models were positively received in all three domains and the identified level of acceptance suggests that the solution is capable of capturing models for training purposes at large.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Di Mitri, D., Schneider, J., Klemke, R., Specht, M., Drachsler, H.: Read between the lines: an annotation tool for multimodal data for learning. In: Proceedings of the 9th International Conference on Learning Analytics and Knowledge, LAK 2019, pp. 51–60. ACM, New York (2019)
Guest, W., et al.: A technology acceptance model for augmented reality and wearable technologies. J. Univ. Comput. Sci. 24(2), 192–219 (2018)
Xue, H., Sharma, P., Wild, F.: User satisfaction in augmented reality-based training using microsoft HoloLens. Computers 8(1), 9 (2019)
Jucks, R., Schulte-Löbbert, P., Bromme, R.: Supporting experts’ written knowledge communication through reflective prompts on the use of specialist concepts. Zeitschrift für Psychol./J. Psychol. 215(4), 237–247 (2007). https://doi.org/10.1027/0044-3409.215.4.237
Langlotz, T., Grubert, J., Grasset, R.: Augmented reality browsers: essential products or only gadgets? CACM 56(11), 34–36 (2013). https://doi.org/10.1145/2527190
Limbu, B., Fominykh, M., Klemke, R., Specht, M., Wild, F.: Supporting training of expertise with wearable technologies: the WEKIT reference framework. In: Yu, S., Ally, M., Tsinakos, A. (eds.) Mobile and Ubiquitous Learning. PRRE, pp. 157–175. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6144-8_10
Limbu, B.H., Jarodzka, H., Klemke, R., Specht, M.: Using sensors and augmented reality to train apprentices using recorded expert performance: a systematic literature review. Educ. Res. Rev. 25, 1–22 (2018). https://doi.org/10.1016/J.EDUREV.2018.07.001
Limbu, B.H., Jarodzka, H., Klemke, R., Wild, F., Specht, M.: From AR to expertise: a user study of an augmented reality training to support expertise development. J. Univ. Comput. Sci. 24(2), 108–128 (2018)
Schneider, J., Di Mitri, D., Limbu, B., Drachsler, H.: Multimodal learning hub: a tool for capturing customizable multimodal learning experiences. In: Pammer-Schindler, V., Pérez-SanagustÃn, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 45–58. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98572-5_4
Acknowledgements
This study is partially funded by the WEKIT project, the Safepat project, and the TCBL project. The WEKIT project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687669. The Safepat project is co-funded by the European Union under the Interreg EMR Programme. The TCBL project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 646133.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Limbu, B., Vovk, A., Jarodzka, H., Klemke, R., Wild, F., Specht, M. (2019). WEKIT.One: A Sensor-Based Augmented Reality System for Experience Capture and Re-enactment. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-29736-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29735-0
Online ISBN: 978-3-030-29736-7
eBook Packages: Computer ScienceComputer Science (R0)