Skip to main content
SpringerLink
Log in

Improving Learnability Capabilities in Desktop VR Medical Applications

  • Conference paper
  • First Online:
HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13095))

Included in the following conference series:

Abstract

The main objective of this study was to evaluate the implicit and explicit learning experiences of two distinct training segments, a tutorial and a Free Play Mode (FPM), of a desktop-based virtual reality (VR) medical operations simulator to assess aspects of learnability for a first-time user. Our goal was to evaluate the tutorial simulator and User Interface (UI) design by interpreting results through the lens of Mayer’s principles of multimedia learning. The experiment was conducted remotely and the study sample comprised of ten upper-year medical students. The video recording from the participant’s desktop camera was retrieved to determine their affective responses by analyzing facial micro-expressions and infer valence pain points (VPPs). Participants performed the simulation’s tutorial followed by the FPM tasks, partitioned into two types: twelve retention tasks designed to verify how well users learned UI elements through the tutorial, and an exploration task to observe how the user explored the interface when few instructions were given. Results showed that the explicit user experience did not differ between the tutorial and the retention tasks. In contrast, users reported significantly higher cognitive load and lower system usability during the exploration task than during the tutorial. A negative correlation was found between perceived self-efficacy and perceived cognitive load. Results pertaining to VPPs indicated that FPM tasks were associated with more negative affective responses when compared to the tutorial. The manuscript concludes with methodological guidelines to assess the learnability of complex, ecologically valid simulations while reinforcing the need to use complementary methods to assess the users’ experience.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

References

  1. Hays, R.T., Jacobs, J.W., Prince, C., Salas, E.: Flight simulator training effectiveness: a meta-analysis. Mil. Psychol. 4(2), 63–74 (1992). https://doi.org/10.1207/s15327876mp0402_1

  2. Izard, S.G., Juanes, J.A., García Peñalvo, F.J., Estella, J.M.G., Ledesma, M.J.S., Ruisoto, P.: Virtual reality as an educational and training tool for medicine. J. Med. Syst. 42(3), 1–5 (2018). https://doi.org/10.1007/s10916-018-0900-2

    Article  Google Scholar 

  3. Wang, P., Wu, P., Wang, J., Chi, H.L., Wang, X.: A critical review of the use of virtual reality in construction engineering education and training. Int. J. Environ. Res. Public Health 15(6) (2018). https://doi.org/10.3390/ijerph15061204

  4. Chittaro, L., Buttussi, F.: Assessing knowledge retention of an immersive serious game vs. a traditional education method in aviation safety. IEEE Trans. Visual Comput. Graph. 21(4), 529–538 (2015)

    Article  Google Scholar 

  5. Makransky, G., Borre-Gude, S., Mayer, R.E.: Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments. J. Comput. Assist. Learn. 35(6), 691–707 (2019). https://doi.org/10.1111/jcal.12375

    Article  Google Scholar 

  6. Mayer, R.E.: Incorporating motivation into multimedia learning. Learn. Instr. 29, 171–173 (2014)

    Article  Google Scholar 

  7. Goodyear, P., Retalis, S.: Learning, technology and design. In: Technology-Enhanced learning: Brill Sense, pp. 1–27 (2010)

    Google Scholar 

  8. Mayer, R.E.: Multimedia Learning, 3rd edn. Cambridge University Press, Cambridge (2020)

    Book  Google Scholar 

  9. Schroeder, R.: Defining virtual worlds and virtual environments. J. Virtual Worlds Res. 1(1) (2008)

    Google Scholar 

  10. Mandal, S.: Brief introduction of virtual reality & its challenges. Int. J. Sci. Eng. Res. 4(4), 304–309 (2013)

    Google Scholar 

  11. Lee, E.A.-L., Wong, K.W.: Learning with desktop virtual reality: low spatial ability learners are more positively affected. Comput. Educ. 79, 49–58 (2014)

    Article  Google Scholar 

  12. Feng, Z., González, V.A., Amor, R., Lovreglio, R., Cabrera-Guerrero, G.: Immersive virtual reality serious games for evacuation training and research: a systematic literature review. Comput. Educ. 127, 252–266 (2018)

    Article  Google Scholar 

  13. McComas, J., MacKay, M., Pivik, J.: Effectiveness of virtual reality for teaching pedestrian safety. Cyberpsychol. Behav. 5(3), 185–190 (2002)

    Article  Google Scholar 

  14. Rose, F.D., Attree, E.A., Brooks, B.M., Parslow, D.M., Penn, P.R.: Training in virtual environments: transfer to real world tasks and equivalence to real task training. Ergonomics 43(4), 494–511 (2000)

    Article  Google Scholar 

  15. Smith, S., Ericson, E.: Using immersive game-based virtual reality to teach fire-safety skills to children. Virtual Real. 13(2), 87–99 (2009)

    Article  Google Scholar 

  16. Rodwin, B.A., et al.: Rate of preventable mortality in hospitalized patients: a systematic review and meta-analysis. J. Gen. Intern. Med. 35(7), 2099–2106 (2020). https://doi.org/10.1007/s11606-019-05592-5

    Article  Google Scholar 

  17. Makled, E., et al.: PathoGenius VR: VR medical training. In: Proceedings of the 8th ACM International Symposium on Pervasive Displays, 2019, pp. 1–2 (2019)

    Google Scholar 

  18. Riener, R., Harders, M.: VR for medical training. In: Virtual Reality in Medicine. Springer, London (2012). https://doi.org/10.1007/978-1-4471-4011-5_8

  19. Bostrom, R.P., Olfman, L., Sein, M.K.: The importance of learning style in end-user training. MIS Q. 14(1), 101–119 (1990). https://doi.org/10.2307/249313

    Article  Google Scholar 

  20. Pellegrino, J.W., Chudowsky, N., Glaser, R.: Knowing what students know: The science and design of educational assessment. ERIC (2001)

    Google Scholar 

  21. Mayer, R.E.: Cognitive theory of multimedia learning. The Cambridge Handbook of Multimedia Learning, vol. 41, pp. 31–48 (2005)

    Google Scholar 

  22. B. ISO and B. STANDARD: Ergonomics of human-system interaction (2010)

    Google Scholar 

  23. Lewis, J.R.: IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. Int. J. Hum. Comput. Interact. 7(1), 57–78 (1995). https://doi.org/10.1080/10447319509526110

  24. Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (eds.) Advances in Psychology, vol. 52, North-Holland, 1988, pp. 139–183 (1988)

    Google Scholar 

  25. Kang, Y.N., Chang, C.H., Kao, C.C., Chen, C.Y., Wu, C.C.: Development of a short and universal learning self-efficacy scale for clinical skills. PLoS ONE 14(1), e0209155 (2019). https://doi.org/10.1371/journal.pone.0209155

    Article  Google Scholar 

  26. Eich, E., Kihlstrom, J.F., Bower, G.H., Forgas, J.P., Niedenthal, P.M.: Cognition and Emotion. Oxford University Press on Demand, Oxford (2000)

    Google Scholar 

  27. Cockburn, A., Quinn, P., Gutwin, C.: Examining the Peak-End Effects of Subjective Experience, presented at the Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea (2015). https://doi.org/10.1145/2702123.2702139

  28. de Guinea, A.O., Titah, R., Léger, P.-M.: Explicit and implicit antecedents of users’ behavioral beliefs in information systems: a neuropsychological investigation. J. Manag. Inf. Syst. 30(4), 179–210 (2014)

    Article  Google Scholar 

  29. Ekman, P., Friesen, W.V.: Manual for the Facial Action Coding System. Consulting Psychologists Press, Palo Alto (1978)

    Google Scholar 

  30. Giroux-Huppé, C., Sénécal, S., Fredette, M., Chen, S.L., Demolin, B., Léger, P.M.: Identifying psychophysiological pain points in the online user journey: the case of online grocery. In: Marcus, A., Wang, W. (eds.) Design, User Experience, and Usability. Practice and Case Studies. HCII 2019. Lecture Notes in Computer Science, vol. 11586, pp. 459–473. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23535-2_34

  31. Paas, F., Tuovinen, J.E., Van Merrienboer, J.J., Darabi, A.A.: A motivational perspective on the relation between mental effort and performance: optimizing learner involvement in instruction. Educ. Tech. Res. Dev. 53(3), 25–34 (2005)

    Article  Google Scholar 

  32. Van Merriënboer, J.J.G., Sweller, J.: Cognitive load theory in health professional education: design principles and strategies. Med. Educ. 44(1), 85–93 (2010). https://doi.org/10.1111/j.1365-2923.2009.03498.x

  33. Sewell, J.L., Boscardin, C.K., Young, J.Q., Ten Cate, O., O’Sullivan, P.S.: Learner, patient, and supervisor features are associated with different types of cognitive load during procedural skills training: implications for teaching and instructional design. Acad. Med. 92(11), 1622–1631 (2017). https://doi.org/10.1097/ACM.0000000000001690

  34. Sweller, J.: Cognitive load theory. In: Psychology of Learning and Motivation, vol. 55, pp. 37–76. Elsevier (2011)

    Google Scholar 

  35. Redifer, J.L., Bae, C.L., Zhao, Q.: Self-efficacy and performance feedback: impacts on cognitive load during creative thinking. Learn. Instr. 71, 101395 (2021). https://doi.org/10.1016/j.learninstruc.2020.101395

  36. Vasile, C., Marhan, A.M., Singer, F.M., Stoicescu, D.: Academic self-efficacy and cognitive load in students. Procedia Soc. Behav. Sci. 12, 478–482 (2011). https://doi.org/10.1016/j.sbspro.2011.02.059

  37. Broder, A.: A taxonomy of web search. In: SIGIR Forum, vol. 36, no. 2 (2002)

    Google Scholar 

  38. Léger, P.M., Courtemanche, F., Fredette, M., Sénécal, S.: A cloud-based lab management and analytics software for triangulated human-centered research. In: Davis, F., Riedl, R., vom Brocke, J., Léger, P.M., Randolph, A. (eds.) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol. 29, pp. 93–99. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01087-4_11

  39. Léger, P.M., et al.: Precision is in the eye of the beholder: application of eye fixation-related potentials to information systems research, 2014. Association for Information Systems (2014)

    Google Scholar 

  40. Loijens, L., Krips, O.: FaceReader methodology note, a white paper by Noldus Information Technology, Noldus. Zugriff am, vol. 16, p. 2018 (2018)

    Google Scholar 

  41. Giroux, F., et al.: Guidelines for collecting automatic facial expression detection data synchronized with a dynamic stimulus in remote moderated user tests. International Conference on Human-Computer Interaction, Forthcoming.

    Google Scholar 

  42. Lamontagne, C., Sénécal, S., Fredette, M., Labonté-LeMoyne, É., Léger, P.M.: The effect of the segmentation of video tutorials on user’s training experience and performance. Comput. Hum. Behav. Rep. 3, 100071 (2021). https://doi.org/10.1016/j.chbr.2021.100071

  43. Dargent, T., Karran, A., Léger, P.M., Coursaris, C.K., Sénécal, S.: The Influence of Task Types on User Experience after a Web Interface Update.

    Google Scholar 

  44. Schunk, D.H., Dibenedetto, M.K.: Self-efficacy theory in education. Handbook of Motivation at School, vol. 2, pp. 34–54 (2016)

    Google Scholar 

  45. Naismith, L.M., Cheung, J.J., Ringsted, C., Cavalcanti, R.B.: Limitations of subjective cognitive load measures in simulation-based procedural training. Med. Educ. 49(8), 805–814 (2015). https://doi.org/10.1111/medu.12732

  46. Brown, E., Cairns, P.: A grounded investigation of game immersion. In: CHI 2004 extended abstracts on Human factors in computing systems, 2004, pp. 1297–1300 (2004)

    Google Scholar 

  47. Qin, H., Rau, P.-L.P., Salvendy, G.: Effects of different scenarios of game difficulty on player immersion. Interact. Comput. 22(3), 230–239 (2010). https://doi.org/10.1016/j.intcom.2009.12.004

    Article  Google Scholar 

  48. Manakhov, P., Ivanov, V.D.: Defining usability problems. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 2016, pp. 3144–3151 (2016)

    Google Scholar 

  49. Mayer, R.E., Fiorella, L.: Principles for reducing extraneous processing in multimedia learning: coherence, signaling, redundancy, spatial contiguity, and temporal contiguity principles. In: Mayer, R.E. (ed.) The Cambridge Handbook of Multimedia Learning, 2 ed. Cambridge Handbooks in Psychology. Cambridge University Press, Cambridge, pp. 279–315 (2014)

    Google Scholar 

  50. Lamontagne, C., et al.: User test: how many users are needed to find the psychophysiological pain points in a journey map? In: Ahram, T., Taiar, R., Colson, S., Choplin, A. (eds.) IHIET 2019. AISC, vol. 1018, pp. 136–142. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-25629-6_22

    Chapter  Google Scholar 

  51. Vasseur, A., et al.: Distributed remote psychophysiological data collection for UX evaluation: a pilot project. In: International Conference on Human-Computer Interaction Forthcoming

    Google Scholar 

  52. DIS, I.: 9241-210: 2010. Ergonomics of human system interaction-Part 210: Human-centred design for interactive systems (formerly known as 13407), International Standardization Organization (ISO). Switzerland (2010)

    Google Scholar 

  53. Charles, R.L., Nixon, J.: Measuring mental workload using physiological measures: a systematic review Appl. Ergon. 74, 221–232 (2019). https://doi.org/10.1016/j.apergo.2018.08.028

  54. Ganglbauer, E., Schrammel, J., Deutsch, S., Tscheligi, M.: Applying psychophysiological methods for measuring user experience: possibilities, challenges and feasibility. In: Workshop on User Experience Evaluation Methods in Product Development, 2009: Citeseer (2009)

    Google Scholar 

  55. Dawson, M.E., Schell, A.M., Filion, D.L.: The electrodermal system (2017)

    Google Scholar 

  56. Sweller, J., Ayres, P., Kalyuga, S.: Measuring cognitive load. In: Sweller, J., Ayres, P., Kalyuga, S. (eds.) Cognitive Load Theory. Explorations in the Learning Sciences, Instructional Systems and Performance Technologies, vol. 1, pp. 71–85. Springer, New York, NY (2011). https://doi.org/10.1007/978-1-4419-8126-4_6

  57. Lackmann, S., Léger, P.-M., Charland, P., Aubé, C., Talbot, J.: The influence of video format on engagement and performance in online learning. Brain Sci. 11(2), 128 (2021)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank CAE Inc for its collaboration and funding as well as the NSERC-PROMPT Industrial Research Chair in UX.

Author information

Authors and Affiliations

  1. HEC Montréal, Montréal, QC, H3T27, Canada

    Laurie-Jade Rochon, Alexander J. Karran, Frédérique Bouvier, Constantinos K. Coursaris, Sylvain Sénécal & Pierre-Majorique Léger

  2. CAE Inc., Saint-Laurent, QC, H4T1G6, Canada

    Jean-François Delisle

Authors
  1. Laurie-Jade Rochon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander J. Karran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frédérique Bouvier
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Constantinos K. Coursaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sylvain Sénécal
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jean-François Delisle
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pierre-Majorique Léger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laurie-Jade Rochon .

Editor information

Editors and Affiliations

  1. University of Creteand Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  3. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA

    Jessie Y. C. Chen

  4. U.S. Army Combat Capabilities Development Command Soldier Center, Orlando, FL, USA

    Gino Fragomeni

  5. Smart Future Initiative, Frankfurt am Main, Hessen, Germany

    Norbert Streitz

  6. Faculty of Arts and Science, Kyushu University, Fukuoka, Japan

    Shin'ichi Konomi

  7. Siemens (United States), Princeton, NJ, USA

    Helmut Degen

  8. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rochon, LJ. et al. (2021). Improving Learnability Capabilities in Desktop VR Medical Applications. In: Stephanidis, C., et al. HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence. HCII 2021. Lecture Notes in Computer Science(), vol 13095. Springer, Cham. https://doi.org/10.1007/978-3-030-90963-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-90963-5_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90962-8

  • Online ISBN: 978-3-030-90963-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation