Cognitive load and mental effort during context switching in augmented reality environments for procedural learning purposes
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Keywords

Realidad aumentada
aprendizaje virtual
esfuerzo mental
informática educativa
entorno de aprendizaje Augmented reality
virtual learning
mental effort
educational computing
learning evironment

How to Cite

Maradei García, F., Bautista Rojas, L. E. ., & Pedraza, G. (2023). Cognitive load and mental effort during context switching in augmented reality environments for procedural learning purposes: [Cognitive load and mental effort during context switching in augmented reality environments for procedural learning purposes]. Pi­xel-Bit. Media and Education Journal, 68, 305–340. https://doi.org/10.12795/pixelbit.97479

Abstract

Because of its ability to display information recorded in space, Augmented Reality is used in procedural learning. However, some authors argue that it may increase users' mental effort by requiring constant shifts in focus of attention between real and virtual content. The objective is to recognize the presence of cognitive load and mental effort when context changes occur during the use of augmented reality in procedural learning tasks, particularly in the participant's peri-personal space. An experiment was conducted to assess subjective cognitive load and mental effort during procedural learning activities for human knee surface anatomy. Subjective measures based on self-reports were combined with objective measures based on pupillometry and eye tracking. The learning activity was evaluated in 34 participants who had no prior experience with the activity. The overall findings revealed that the Subjective Cognitive Load and eye tracking measures differ significantly between treatments. The pupillometry measurement, on the other hand, revealed no differences.

https://doi.org/10.12795/pixelbit.97479
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References

Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013). A survey on ambient intelligence in healthcare. Proceedings of the IEEE, 101(12), 2470–2494. https://doi.org/10.1109/JPROC.2013.2262913

Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829–839. https://doi.org/10.1038/nrn1201

Brunken, R., Plass, J. L., Leutner, D., & Brünken, R. (2010). Direct Measurement of Cognitive Load in Multimedia Learning Direct Measurement of Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1), 53–61. https://doi.org/https://doi.org/10.1207/S15326985EP3801_7

Condino, S., Carbone, M., Piazza, R., Ferrari, M., & Ferrari, V. (2019). Perceptual Limits of Optical See-Through Visors for Augmented Reality Guidance of Manual Tasks. IEEE Transactions on Biomedical Engineering, 67(2), 411–419. https://doi.org/10.1109/TBME.2019.2914517

Dankelman, J., Chmarra, M. K., Verdaasdonk, E. G. G., Stassen, L. P. S., & Grimbergen, C. A. (2005). Fundamental aspects of learning minimally invasive surgical skills. Minimally Invasive Therapy & Allied Technologies, 14(4–5), 247–256. https://doi.org/10.1080/13645700500272413

Eiberger, A., Kristensson, P. O., Mayr, S., Kranz, M., & Grubert, J. (2019). Effects of Depth Layer Switching between an Optical See-Through Head-Mounted Display and a Body-Proximate Display. Symposium on Spatial User Interaction, 1–9. https://doi.org/10.1145/3357251.3357588

Evans, G., Miller, J., Iglesias Pena, M., MacAllister, A., & Winer, E. (2017). Evaluating the Microsoft HoloLens through an augmented reality assembly application. 10197, 101970V. https://doi.org/10.1117/12.2262626

Gabbard, J. L. Mehra, D. G. Swan, J. E. (2019). Effects of ar display context switching and focal distance switching on human performance. IEEE Trans Vis Comput Graph, 25, 2228–2241. https://doi.org/10.1109/tvcg.2018.2832633

Goldberg, J. H., & Wichansky, A. M. (2003). Eye Tracking in Usability Evaluation. A Practitioner’s Guide. The Mind’s Eye: Cognitive and Applied Aspects of Eye Movement Research, January 2003, 493–516. https://doi.org/10.1016/B978-044451020-4/50027-X

Gupta, D. (2004). An Empirical Study of the Effects of Context-Switch, Object Distance, and Focus Depth on Human Performance in Augmented Reality. Virginia Polytechnic Institute and State University. http://hdl.handle.net/10919/33507

Henderson, S. J. (2011). Augmented Reality Interfaces for Procedural Tasks. Dissertation.

Hennessy, R.T. Iida, T. Shiina, K. Leibowitz, H. W. (1976). The effect of pupil size on accommodation. Vision Res, 16, 587–589. https://doi.org/10.1016/0042-6989(76)90004-3

Huckauf, A. Urbina, M. H. Böckelmann, I. et al. (2010). Perceptual issues in optical-see-through displays. Proc - APGV 2010 Symp Appl Percept Graph Vis, 1, 41–48. https://doi.org/10.1145/1836248.1836255

Jarodzka, H., Janssen, N., Kirschner, P. A., & Erkens, G. (2015). Avoiding split attention in computer-based testing: Is neglecting additional information facilitative? British Journal of Educational Technology, 46(4), 803–817. https://doi.org/10.1111/bjet.12174

Klepsch, M., & Seufert, T. (2020). Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. In Instructional Science (Vol. 48, Issue 1). Springer Netherlands. https://doi.org/10.1007/s11251-020-09502-9

Kret, M. E. Sjak-Shie, E. E. (2019). Preprocessing pupil size data: Guidelines and code. Behav Res Methods, 51, 1336–1342. https://doi.org/10.3758/s13428-018-1075-y

Korbach, A., Brünken, R., & Park, B. (2017). Measurement of cognitive load in multimedia learning: a comparison of different objective measures. Instructional Science, 45(4), 515–536. https://doi.org/10.1007/s11251-017-9413-5

Labs, M. R. (2022). Mixed Reality - User Experience Confort. Micorsoft Documentation.

Lim, J., Reiser, R. A., & Olina, Z. (2009). The effects of part-task and whole-task instructional approaches on acquisition and transfer of a complex cognitive skill. Educational Technology Research and Development, 57(1), 61–77. https://doi.org/10.1007/s11423-007-9085-y

Liu, J. C., Li, K. A., Yeh, S. L., & Chien, S. Y. (2022). Assessing Perceptual Load and Cognitive Load by Fixation-Related Information of Eye Movements. Sensors, 22(3). https://doi.org/10.3390/s22031187

Andrade, L. (2012). Teoría de la carga cognitiva, diseño multimedia y aprendizaje: un estado del arte. Magis. Revista Internacional de Investigación En Educación, 5, 75–92.

Maggio, L. A., Cate, O. Ten, Irby, D. M., & O’Brien, B. C. (2015). Designing evidence-based medicine training to optimize the transfer of skills from the classroom to clinical practice: Applying the four component instructional design model. Academic Medicine, 90(11), 1457–1461. https://doi.org/10.1097/ACM.0000000000000769

Majooni, A., Masood, M., & Akhavan, A. (2016). An eye tracking experiment on strategies to minimize the redundancy and split attention effects in scientific graphs and diagrams. Advances in Intelligent Systems and Computing, 500, 529–540. https://doi.org/10.1007/978-3-319-41962-6_47

Mark, G., Gudith, D., & Klocke, U. (2008). The cost of interrupted work: More speed and stress. Conference on Human Factors in Computing Systems - Proceedings, 107–110. https://doi.org/10.1145/1357054.1357072

Melo, M. (2018). The 4C/ID-Model in Physics Education: Instructional Design of a Digital Learning Environment to Teach Electrical Circuits. International Journal of Instruction, 11(1), 103–122. https://doi.org/10.12973/iji.2018.1118a

Melo, M., & Miranda, G. L. (2016). Efeito do modelo 4C/ID sobre a aquisição e transferência de aprendizagem: Revisão de literatura com meta-análise. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 18, 114–130. https://doi.org/10.17013/risti.18.114-130

Paas, F., Tuovinen, J., Tabbers, H., & Van Gerven, P. W. M. (2010). Cognitive Load Measurement as a Means to Advance Cognitive Load Theory. Educational Psychologist, 1520(38), 43–52. https://doi.org/10.1207/S15326985EP3801

Plass, J. L. Moreno, R. Brünken, R. (2010). Cognitive load theory.

Rashid, U., Kauko, J., Häkkilä, J., & Quigley, A. (2011). Proximal and distal selection of widgets: Designing distributed UI for mobile interaction with large display. Mobile HCI 2011 - 13th International Conference on Human-Computer Interaction with Mobile Devices and Services, 495–498. https://doi.org/10.1145/2037373.2037446

Reid, G. B. Nygren, T. E. (1988). Human Mental Workload.

Santos, M. E. C., Chen, A., Taketomi, T., Yamamoto, G., Miyazaki, J., & Kato, H. (2014). Augmented reality learning experiences: Survey of prototype design and evaluation. IEEE Transactions on Learning Technologies, 7(1), 38–56. https://doi.org/10.1109/TLT.2013.37

Sweller, J. (2018). Measuring cognitive load. 1–2. https://doi.org/10.1007/s40037-017-0395-4

Tao, D., Tan, H., Wang, H., Zhang, X., Qu, X., & Zhang, T. (2019). A systematic review of physiological measures of mental workload. International Journal of Environmental Research and Public Health, 16(15), 1–23. https://doi.org/10.3390/ijerph16152716

Vandewaetere, M., Manhaeve, D., Aertgeerts, B., Clarebout, G., Van Merriënboer, J. J. G., & Roex, A. (2015). 4C/ID in medical education: How to design an educational program based on whole-task learning: AMEE Guide No. 93. Medical Teacher, 37(1), 4–20. https://doi.org/10.3109/0142159X.2014.928407

Zagermann, J., Pfeil, U., & Reiterer, H. (2016). Measuring cognitive load using eye tracking technology in visual computing. ACM International Conference Proceeding Series, 24-October, 78–85. https://doi.org/10.1145/2993901.2993908

Zu, T., Hutson, J., Loschky, L. C., & Sanjay Rebello, N. (2018). Use of eye-tracking technology to investigate cognitive load theory. ArXiv, 472–475. https://doi.org/10.1119/perc.2017.pr.113

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