Advertisement

3D learning spaces and activities fostering users’ learning, acceptance, and creativity

  • Donghee ShinEmail author
  • Seyoung Park
Article
  • 2 Downloads

Abstract

As an instructional material, 3D instructions afford people to learn procedural-manipulative tasks. Observing and emulating motions presented in 3D animations is important in learning contexts. This study examined the effects of visual cueing in an effort to identify the optimal way to present information in a 3D virtual environment. While previous studies have found that animations are more effective than static images for learning procedural-manipulative tasks, the transient nature of dynamic visualizations might create an unnecessary cognitive load on learners. To compensate strategically for the lack of permanency, this study suggests combining dynamic and static visualizations into one medium by adding transparent static images (visual cueing) to an animation. A between-subjects experiment was conducted to examine the effects of visual cueing on cognitive load and learning outcomes. The study found that the hybrid of dynamic and static visualizations was beneficial for reducing cognitive load, although it did not improve learning outcomes. The results suggest a design strategy for improving the effectiveness of 3D instructional animations. The findings are broadly applicable to numerous learning contexts, such as virtual reality storytelling, augmented reality games, and diverse gamification services.

Keywords

Visual cueing 3D animation Procedural-manipulative task Animation versus static images Cognitive load theory Multimedia learning 

Notes

Compliance with ethical standards

Conflict of interest

The author declares that there is no conflict of interest regarding the publication of this article.

References

  1. Arguel, A., & Jamet, E. (2009). Using video and static pictures to improve learning of procedural contents. Computers in Human Behavior, 25(2), 354–359.CrossRefGoogle Scholar
  2. Ayres, P., Marcus, N., Chan, C., & Qian, N. (2009). Learning hand manipulative tasks: When instructional animations are superior to equivalent static representations. Computers in Human Behavior, 25(2), 348–353.CrossRefGoogle Scholar
  3. Ayres, P., & Paas, F. (2007). Making instructional animations more effective: A cognitive load approach. Applied Cognitive Psychology, 21(6), 695–700.CrossRefGoogle Scholar
  4. Ayres, P., & Sweller, J. (2005). The split-attention principle in multimedia learning. The Cambridge Handbook of Multimedia Learning, 2, 135–146.CrossRefGoogle Scholar
  5. Berry, D., & Dienes, Z. P. (1993). Implicit learning: Theoretical and empirical issues. New York, NY: Psychology Press.Google Scholar
  6. Brunken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38(1), 53–61.CrossRefGoogle Scholar
  7. Castro-Alonso, J. C., Ayres, P., & Paas, F. (2015). Animations showing Lego manipulative tasks: Three potential moderators of effectiveness. Computers & Education, 85, 1–13.CrossRefGoogle Scholar
  8. Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8(4), 293–332.CrossRefGoogle Scholar
  9. De Koning, B. B., Tabbers, H. K., Rikers, R. M., & Paas, F. (2009). Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychology Review, 21(2), 113–140.CrossRefGoogle Scholar
  10. De Koning, B. B., Tabbers, H. K., Rikers, R. M., & Paas, F. (2011). Attention cueing in an instructional animation: The role of presentation speed. Computers in Human Behavior, 27(1), 41–45.CrossRefGoogle Scholar
  11. Garland, T. B., & Sanchez, C. A. (2013). Rotational perspective and learning procedural tasks from dynamic media. Computers & Education, 69, 31–37.CrossRefGoogle Scholar
  12. Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index). Advances in Psychology, 52, 139–183.CrossRefGoogle Scholar
  13. Hegarty, M. (2004). Dynamic visualizations and learning: Getting to the difficult questions. Learning and Instruction, 14(3), 343–351.CrossRefGoogle Scholar
  14. Höffler, T. N., & Leutner, D. (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17(6), 722–738.CrossRefGoogle Scholar
  15. Hwang, Y., & Shin, D. (2018). The roles of visual experience in enhancing user performance in virtual environments. Social Behavior and Personality, 46(1), 11–24.  https://doi.org/10.2224/sbp.6500.CrossRefGoogle Scholar
  16. Mayer, R. E. (2010). Unique contributions of eye-tracking research to the study of learning with graphics. Learning and Instruction, 20(2), 167–171.CrossRefGoogle Scholar
  17. Mayer, R. E., & Moreno, R. (2002). Animation as an aid to multimedia learning. Educational Psychology Review, 14(1), 87–99.CrossRefGoogle Scholar
  18. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.CrossRefGoogle Scholar
  19. Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24(1), 27–45.CrossRefGoogle Scholar
  20. Paas, F., Tuovinen, J., Tabbers, H., & van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38, 63–71.CrossRefGoogle Scholar
  21. Ryle, G. (2009). The concept of mind. New York, NY: Routledge.CrossRefGoogle Scholar
  22. Shin, D. (2012). 3DTV as a social platform for communication and interaction. Information Technology and People, 25(1), 55–80.CrossRefGoogle Scholar
  23. Shin, D. (2017). The role of affordance in the experience of virtual reality learning. Telematics and Informatics, 34(8), 1826–1836.  https://doi.org/10.1016/j.tele.2017.05.013.CrossRefGoogle Scholar
  24. Shin, D., An, H., & Kim, J. (2016a). How the second screens change the way people interact and learn: The effects of second screen use on information processing. Interactive Learning Environment, 24(8), 2058–2079.  https://doi.org/10.1080/10494820.2015.1076851.CrossRefGoogle Scholar
  25. Shin, D., Choi, M., Kim, J., & Lee, J. (2016b). Interaction, engagement, and perceived interactivity in single-handed interaction. Internet Research, 26(5), 1134–1157.  https://doi.org/10.1108/IntR-12-2014-0312.CrossRefGoogle Scholar
  26. Shin, D., & Chung, K. (2017). The effects of input modality and story-based knowledge on users’ game experience. Computers in Human Behavior, 68, 180–189.  https://doi.org/10.1016/j.chb.2016.11.030.CrossRefGoogle Scholar
  27. Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human–Computer Studies, 57(4), 247–262.CrossRefGoogle Scholar
  28. Van Gog, T., Paas, F., Marcus, N., Ayres, P., & Sweller, J. (2009). The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review, 21(1), 21–30.CrossRefGoogle Scholar
  29. Wong, A., Marcus, N., Ayres, P., Smith, L., Cooper, G. A., Paas, F., et al. (2009). Instructional animations can be superior to statics when learning human motor skills. Computers in Human Behavior, 25(2), 339–347.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Communication and Media SciencesZayed UniversityAbu DhabiUnited Arab Emirates
  2. 2.School of CommunicationSungkyunkwan UniversitySeoulKorea

Personalised recommendations