Métricas alternativas
Palabras clave
aprendizaje virtual
esfuerzo mental
informática educativa
entorno de aprendizaje Augmented reality
virtual learning
mental effort
educational computing
learning evironment
Cómo citar
Resumen
La Realidad Aumentada es usada en el aprendizaje procedimental por su capacidad de mostrar información registrada en el espacio. Sin embargo, algunos autores argumentan que podría incrementar el esfuerzo mental de los usuarios, al requerir cambios continuos en el enfoque de atención entre el contenido real y el contenido virtual. El objetivo es conocer la presencia de carga cognitiva y esfuerzo mental, cuando se presentan cambios de contexto en el uso de realidad aumentada en tareas de aprendizaje procedimental, especialmente en el espacio peri-personal del participante. Se ejecutó un experimento para evaluar la carga cognitiva subjetiva y el esfuerzo mental, durante actividades de aprendizaje procedimental de anatomía de superficie de la rodilla humana. Se combinaron medidas subjetivas basadas en auto-reportes, y medidas objetivas, basadas en pupilometría y rastreo ocular. Se evaluó la actividad de aprendizaje en treinta y cuatro participantes sin experiencia en la actividad. Los resultados generales mostraron que la medida de Carga Cognitiva Subjetiva y las medidas de rastreo ocular, evidencian diferencias significativas entre los tratamientos. Sin embargo, la medida de pupilometría no mostró diferencias.
Citas
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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