Palabras clave
Neuroeducación, patrones de aprendizaje, seguimiento ocular, aprendizaje personalizado, análisis de clúster, tecnología educativa
Resumen
Los avances neurotecnológicos están posibilitando en los contextos educativos nuevos conocimientos sobre la forma de aprender de cada estudiante. No obstante, su aplicación plantea retos para la docencia en contextos naturales. En este trabajo se presenta un ejemplo de uso y aplicabilidad de la tecnología de seguimiento ocular en el ámbito de la Educación Superior. Se trabajó con una muestra de 20 estudiantes de tres universidades (Burgos y Valladolid en España y Miño en Portugal). Los objetivos fueron: 1) comprobar si existían diferencias significativas en indicadores de esfuerzo cognitivo (FC, FD, SC, PD, VC) hallados con la tecnología de seguimiento ocular entre estudiantes con y sin conocimientos previos; 2) comprobar si existían clústeres de patrones de conductas de aprendizaje entre los estudiantes; 3) analizar diferencias en la visualización de los patrones de conducta. Se utilizó un diseño cuasiexperimental sin grupo control y un diseño descriptivo. Los resultados indicaron diferencias significativas entre los estudiantes con y sin conocimientos previos respecto de los resultados de aprendizaje. También, se hallaron dos tipos de clústeres en los indicadores de esfuerzo cognitivo. Finalmente, se efectuó un análisis comparativo sobre los patrones de conducta de aprendizaje en estudiantes del clúster 1 vs. clúster 2. El uso de la tecnología de seguimiento ocular posibilita el registro de un gran volumen de datos respecto del proceso de aprendizaje. No obstante, en la actualidad su uso en contextos educativos naturales exige al profesorado conocimientos tecnológicos y de minería de datos.
Referencias
Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers and Education, 125, 413-428. https://doi.org/10.1016/j.compedu.2018.06.023
Link DOI | Link Google Scholar
Asadi, A., Saeedpour-Parizi, M.R., Aiken, C.A., Jahanbani, Z., Houminiyan-Sharif-Abadi, D., Simpson, T., & Marchant, D. (2022). Effects of attentional focus and cognitive load on novice dart throwing: Evidence from quiet eye duration and pupillary responses. Human Movement Science, 86, 103015. https://doi.org/10.1016/j.humov.2022.103015
Link DOI | Link Google Scholar
Azevedo, R., & Gaševi?, D. (2019). Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges. Computers in Human Behavior, 96, 207-210. https://doi.org/10.1016/j.chb.2019.03.025
Link DOI | Link Google Scholar
Bogarín, A., Cerezo, R., & Romero, C. (2018). A survey on educational process mining. Wiley Interdisciplinary Reviews, 8(1). https://doi.org/10.1002/widm.1230
Link DOI | Link Google Scholar
Campbell, D.F., & Stanley, J. (2005). Diseños experimentales y cuasiexperimentales en la investigación social.. Amorrortu. https://bit.ly/3JJDubN
Link Google Scholar
Chango, W., Lara, J.A., Cerezo, R., & Romero, C. (2022). A review on data fusion in multimodal learning analytics and educational data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(4), 1-19. https://doi.org/10.1002/widm.1458
Link DOI | Link Google Scholar
Chemerys, H.Y., & Ponomarenko, O.V. (2022). Opportunities and prospects for personalizing the user interface of the educational platform in accordance with the personality psychotypes. Advances in Computational Design, 7(2), 139-151. https://doi.org/10.12989/acd.2022.7.2.139
Link DOI | Link Google Scholar
Coskun, A., & Cagiltay, K. (2022). A systematic review of eye-tracking-based research on animated multimedia learning. Journal of Computer Assisted Learning, 38(2), 581-598. https://doi.org/10.1111/jcal.12629
Link DOI | Link Google Scholar
Demšar, J., Curk, T., Erjavec, A., Gorup, ?., Ho?evar, T., Milutinovi?, M., Možina, M., Polajnar, M., Toplak, M., Stari?, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., & Zupan, B. (2013). Orange: data mining toolbox in Python. The Journal of Machine Learning Research, 14(1), 2349-2353. https://bit.ly/3yNtGHi
Link Google Scholar
Díez-Pastor, J.F., García-Osorio, C., & Rodríguez, J.J. (2014). Tree ensemble construction using a GRASP-based heuristic and annealed randomness. Information Fusion, 20(1), 189-202. https://doi.org/10.1016/j.inffus.2014.01.009
Link DOI | Link Google Scholar
Diwanji, V.S. (2022). Improving accessibility and inclusiveness of university websites for international students: a mixed-methods usability assessment. Technology, Pedagogy and Education, 32(1), 1-26. https://doi.org/10.1080/1475939X.2022.2089724
Link DOI | Link Google Scholar
Elliott, L.J., Lum, H.C., Aqlan, F., Zhao, R., & Lasher, C.D. (2020). A Study of Metacognitive Problem Solving in Undergraduate Engineering Students. In W. Karwowski, T. Ahram, & S. Nazir (Eds.), Advances in Intelligent Systems and Computing (pp. 95-102). Springer International Publishing. https://doi.org/10.1007/978-3-030-20135-7_9
Link DOI | Link Google Scholar
Feng, S., & Law, N. (2021). Mapping artificial intelligence in education research: A network?based keyword analysis. International Journal of Artificial Intelligence in Education, 31, 277-303. https://doi.org/10.1007/s40593-021-00244-4
Link DOI | Link Google Scholar
Fiorella, L. (2022). Learning by explaining after pauses in video lectures: Are provided visuals a scaffold or a crutch? Applied Cognitive Psychology, 36(5), 1142-1149. https://doi.org/10.1002/acp.3994
Link DOI | Link Google Scholar
Giannakos, M.N., Sharma, K., Pappas, I.O., Kostakos, V., & Velloso, E. (2019). Multimodal data as a means to understand the learning experience. International Journal of Information Management, 48, 108-119. https://doi.org/10.1016/j.ijinfomgt.2019.02.003
Link DOI | Link Google Scholar
He, D., Wang, Z., Khalil, E.B., Donmez, B., Qiao, G., & Kumar, S. (2022). Classification of Driver Cognitive Load: Exploring the Benefits of Fusing Eye-Tracking and Physiological Measures. Transportation Research Record: Journal of the Transportation Research Board, 2676(10), 670-681. https://doi.org/10.1177/03611981221090937
Link DOI | Link Google Scholar
IBM Corp (Ed.) (2022). SPSS Statistical Package for the Social Sciences (SPSS) (Versión 28). [Software]. IBM. https://ibm.co/3hWIls7
Link Google Scholar
Joe-Louis-Paul, I., Sasirekha, S., Uma-Maheswari, S., Ajith, K.A.M., Arjun, S.M., & Athesh-Kumar, S. (2019). Eye Gaze Tracking-Based Adaptive E-learning for Enhancing Teaching and Learning in Virtual Classrooms. In S. Fong, S. Akashe, & P.N. Mahalle (Eds.), Information and Communication Technology for Competitive Strategies (pp. 165-176). Springer Singapore. https://doi.org/10.1007/978-981-13-0586-3
Link DOI | Link Google Scholar
Kao, G Y.M., Chiang, X.Z., & Foulsham, T. (2019). Reading behavior and the effect of embedded selfies in role-playing picture e-books: An eye-tracking investigation. Computers and Education, 136, 99-112. https://doi.org/10.1016/j.compedu.2019.03.010
Link DOI | Link Google Scholar
Kuhnel, M., Seiler, L., Honal, A., & Ifenthaler, D. (2018). Mobile learning analytics in higher education: Usability testing and evaluation of an app prototype. Interactive Technology and Smart Education, 15(4), 332-347. https://doi.org/10.1108/ITSE-04-2018-0024
Link DOI | Link Google Scholar
Kulomäki, J., Oksama, L., Rantanen, E., & Hyönä, J. (2022). Attention control in a demanding dynamic time-sharing environment: An eye-tracking study. Attention, Perception, and Psychophysics, 84, 352-371. https://doi.org/10.3758/s13414-021-02377-z
Link DOI | Link Google Scholar
Luo, W., & Zhou, R. (2020). Can working memory task-related EEG biomarkers measure fluid intelligence and predict academic achievement in healthy children? Frontiers in Behavioral Neuroscience, 14, 1-14. https://doi.org/10.3389/fnbeh.2020.00002
Link DOI | Link Google Scholar
McLeod, G., McKendrick, M., Tafili, T., Obregon, M., Neary, R., Mustafa, A., Raju, P., Kean, D., McKendrick, G., & McKendrick, T. (2022). Patterns of skills acquisition in anesthesiologists during simulated interscalene block training on a soft embalmed thiel cadaver: Cohort study. JMIR Medical Education, 8(3), 1-21. https://doi.org/10.2196/32840
Link DOI | Link Google Scholar
Merchie, E., Heirweg, S., & Van-Keer, H. (2022). Mind maps: Processed as intuitively as thought? Investigating late elementary students’ eye-tracked visual behavior patterns in-depth. Frontiers in Psychology, 13, 1-18. https://doi.org/10.3389/fpsyg.2022.821768
Link DOI | Link Google Scholar
Mills, B.W., Carter, O.B.J., Rudd, C.J., Claxton, L.A., Ross, N.P., & Strobel, N.A. (2016). Effects of low-versus high-fidelity simulations on the cognitive burden and performance of entry-level paramedicine students: A mixed-methods comparison trial using eye-tracking, continuous heart rate, difficulty rating scales, video observation and inter. Simulation in Healthcare, 11(1), 10-18. https://doi.org/10.1097/SIH.0000000000000119
Link DOI | Link Google Scholar
Molina, A.I., Navarro, Ó., Lacruz, M., & Ortega, M. (2017). El empleo de técnicas de seguimiento ocular para evaluar materiales educativos en Educación Primaria. Revista de Educación, 376, 87-109. https://doi.org/10.4438/1988-592X-RE-2017-376-345
Link DOI | Link Google Scholar
Ollesch, L., Heimbuch, S., & Bodemer, D. (2021). Improving learning and writing outcomes: Influence of cognitive and behavioral group awareness tools in wikis. International Journal of Computer-Supported Collaborative Learning, 16, 225-259. https://doi.org/10.1007/s11412-021-09346-6
Link DOI | Link Google Scholar
Pi, Z., & Hong, J. (2016). Learning process and learning outcomes of video podcasts including the instructor and PPT slides: A Chinese case. Innovations in Education and Teaching International, 53(2), 135-144. https://doi.org/10.1080/14703297.2015.1060133
Link DOI | Link Google Scholar
Prokop, M., Pila?, L., & Tichá, I. (2020). Impact of think-aloud on eye-tracking: A comparison of concurrent and retrospective think-aloud for research on decision-making in the game environment. Sensors, 20(10). https://doi.org/10.3390/s20102750
Link DOI | Link Google Scholar
Rodziewicz-Cybulska, A., Krejtz, K., Duchowski, A.T., & Krejtz, I. (2022). Measuring cognitive effort with pupillary activity and fixational eye movements when reading: longitudinal comparison of children with and without primary music education. In F. Shic, E. Kasneci, M, Khamis, H. Gellersen, K, Krejtz, D. Weiskopf ... & S. Eivazi (Eds.), ETRA ’22: 2022 Symposium on Eye Tracking Research and Applications (pp. 1-18). https://doi.org/10.1145/3517031.3529636
Link DOI | Link Google Scholar
Rother, A., & Spliopoulou, M. (2022). Virtual reality for medical annotation tasks: A systematic review. Frontiers in Virtual Reality, 3, 1-12. https://doi.org/10.3389/frvir.2022.717383
Link DOI | Link Google Scholar
Sáiz-Manzanares, M.C., García-Osorio, C.I., Díez-Pastor, J.F., & Martín-Antón, L.J. (2019). Will personalized e-Learning increase deep learning in higher education? Information Discovery and Delivery, 47(1), 53-63. https://doi.org/10.1108/IDD-08-2018-0039
Link DOI | Link Google Scholar
Sáiz-Manzanares, M.C., Payo-Hernanz, R.J., Zaparaín-Yáñez, M.J., Andrés-López, G., Marticorena-Sánchez, R., Calvo-Rodríguez, A., Martín, C., & Rodríguez-Arribas, S. (2021b). Eye-tracking technology and data-mining techniques used for a behavioral analysis of adults engaged in learning processes. Journal of Visualized Experiments, 172 (e62103), 1-16. https://doi.org/10.3791/62103
Link DOI | Link Google Scholar
Sáiz-Manzanares, M.C., Pérez, I.R., Rodríguez, A.A., Arribas, S.R., Almeida, L., & Martin, C.F. (2021a). Analysis of the learning process through eye tracking technology and feature selection techniques. Applied Sciences, 11(13). https://doi.org/10.3390/app11136157
Link DOI | Link Google Scholar
Sáiz-Manzanares, M.C., Rodríguez Diez, J.J., Marticorena-Sánchez, R., Zaparaín-Yáñez, M.J., & Cerezo Menéndez, R. (2020). Lifelong learning from sustainable education: An analysis with eye tracking and data mining techniques. Sustainability, 12(5). https://doi.org/10.3390/su12051970
Link DOI | Link Google Scholar
Scharinger, C., Schüler, A., & Gerjets, P. (2020). Using eye-tracking and EEG to study the mental processing demands during learning of text-picture combinations. International Journal of Psychophysiology, 158, 201-214. https://doi.org/10.1016/j.ijpsycho.2020.09.014
Link DOI | Link Google Scholar
Schweizer, T., Wyss, T., & Gilgen-Ammann, R. (2022). Detecting soldiers’ fatigue using eye-tracking glasses: Practical field applications and research opportunities. Military Medicine, 187 (11-12), e1330-e1337. https://doi.org/10.1093/milmed/usab509
Link DOI | Link Google Scholar
Seifert, L., Cordier, R., Orth, D., Courtine, Y., & Croft, J. L. (2017). Role of route previewing strategies on climbing fluency and exploratory movements. PLoS ONE, 12(4), 1-22. https://doi.org/10.1371/journal.pone.0176306
Link DOI | Link Google Scholar
Seinen, T.M., Fridgeirsson, E.A., Ioannou, S., Jeannetot, D., John, L.H., Kors, J.A., Markus, A.F., Pera, V., Rekkas, A., Williams, R.D., Yang, C., Van Mulligen, E.M., & Rijnbeek, P.R. (2022). Use of unstructured text in prognostic clinical prediction models: A systematic review. Journal of the American Medical Informatics Association, 29(7), 1292-1302. https://doi.org/10.1093/jamia/ocac058
Link DOI | Link Google Scholar
Shojaeizadeh, M., Djamasbi, S., Paffenroth, R.C., & Trapp, A.C. (2019). Detecting task demand via an eye tracking machine learning system. Decision Support Systems, 116, 91-101. https://doi.org/https://doi.org/10.1016/j.dss.2018.10.012
Link Google Scholar
Souchet, A.D., Philippe, S., Lourdeaux, D., & Leroy, L. (2022). Measuring Visual Fatigue and Cognitive Load via Eye Tracking while Learning with Virtual Reality Head-Mounted Displays: A Review. International Journal of Human-Computer Interaction, 38(9), 801-824. https://doi.org/10.1080/10447318.2021.1976509
Link DOI | Link Google Scholar
Taub, M., & Azevedo, R. (2019). How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System? International Journal of Artificial Intelligence in Education, 29(1), 1-28. https://doi.org/10.1007/s40593-018-0165-4
Link DOI | Link Google Scholar
Taub, M., Mudrick, N.V., Azevedo, R., Millar, G.C., Rowe, J., & Lester, J. (2017). Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island. Computers in Human Behavior, 76, 641-655. https://doi.org/10.1016/j.chb.2017.01.038
Link DOI | Link Google Scholar
Thees, M., Altmeyer, K., Kapp, S., Rexigel, E., Beil, F., Klein, P., Malone, S., Brünken, R., & Kuhn, J. (2022). Augmented reality for presenting real-time data during students’ laboratory work: comparing a head-mounted display with a separate display. Frontiers in Psychology, 13, 1-16. https://doi.org/10.3389/fpsyg.2022.804742
Link DOI | Link Google Scholar
Tobii Pro Lab (Ed.)(2023). User Manual v.1.194. https://bit.ly/41KjHQm
Link Google Scholar
Tsai, M.J., Wu, A.H., Bråten, I., & Wang, C.Y. (2022). What do critical reading strategies look like? Eye-tracking and lag sequential analysis reveal attention to data and reasoning when reading conflicting information. Computers and Education, 187, 104544. https://doi.org/10.1016/j.compedu.2022.104544
Link DOI | Link Google Scholar
Van-Marlen, T., van Wermeskerken, M., Jarodzka, H., & van-Gog, T. (2018). Effectiveness of eye movement modeling examples in problem solving: The role of verbal ambiguity and prior knowledge. Learning and Instruction, 58, 274-283. https://doi.org/10.1016/j.learninstruc.2018.07.005
Link DOI | Link Google Scholar
Yang, F.Y., & Wang, H.Y. (2023). Tracking visual attention during learning of complex science concepts with augmented 3D visualizations. Computers & Education, 193, 104659. https://doi.org/10.1016/j.compedu.2022.104659
Link DOI | Link Google Scholar
Ficha técnica
Recibido: 10-12-2022
Revisado: 11-01-2023
Aceptado: 23-02-2023
OnlineFirst: 30-05-2023
Fecha publicación: 01-07-2023
Tiempo de revisión del artículo : 32 (en días) | Media de tiempo de revisión de los manuscritos del número 76: -6 (en días)
Tiempo de aceptación del artículo: 75 (en días) | Media tiempo aceptación de los manuscritos del número 76: 72 (en días)
Tiempo de edición OnlineFirst: 157 (en días) | Media tiempo edición de los OnlineFirst del número 76: 154 (en días)
Tiempo de publicacicón final del artículo: 202 (en días) | Media tiempo de publicación final de los articulos del número 76: 199 (en días)
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María-Consuelo Sáiz-Manzanares, Burgos (España)
