Ir al contenido

Documat

Aplicación y retos de la tecnología de movimiento ocular en Educación Superior

  • María Consuelo Sáiz-Manzanares [1] ; Raúl Marticorena-Sánchez [1] ; Luis-J. Martín-Antón [2] ; Leandro Almeida [3] ; Miguel-Ángel Carbonero-Martín [2]
    1. [1] Universidad de Burgos

      Universidad de Burgos

      Burgos, España

    2. [2] Universidad de Valladolid

      Universidad de Valladolid

      Valladolid, España

    3. [3] Universidade do Minho

      Universidade do Minho

      Braga (São José de São Lázaro), Portugal

    Mostrar afiliaciones +
  • Localización: Comunicar: Revista Científica de Comunicación y Educación, ISSN 1134-3478, Nº 76, 2023 (Ejemplar dedicado a: Neurotecnología en el aula: Investigación actual y futuro potencial), págs. 35-46
  • Idioma: español
  • DOI: 10.3916/C76-2023-03
  • Títulos paralelos:
    • Application and challenges of eye tracking technology in Higher Education
  • Enlaces
  • Resumen
    • español

      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.

    • English

      Advances in neuro-technology provide new insights into how individual students learn in educational contexts. However, applying it poses challenges for teachers in natural settings. This paper presents an example of the use and applicability of eye-tracking technology in Higher Education. We worked with a sample of 20 students from three universities (Burgos and Valladolid in Spain and Miño in Portugal). The objectives were: (1) to determine whether there were significant differences in indicators of cognitive effort (FC, FD, SC, PD, VC) found with eye-tracking technology between students with and without prior knowledge; (2) to determine whether there were clusters of learning behavior patterns among students; and (3) to analyze differences in the visualization of behavior patterns. A quasi-experimental design without a control group and a descriptive design were used. The results indicated significant differences in learning outcomes between students with and without prior knowledge. In addition, two clusters were found in indicators of cognitive effort. Finally, a comparative analysis of learning behavior patterns between students in cluster 1 vs. cluster 2 was performed. Eye-tracking technology makes it possible to record large data about the learning process. However, using it in natural educational settings currently requires teachers to have technological and data mining skills.

  • Referencias bibliográficas
    • Alemdag, E., & Cagiltay, K. (2018). A systematic review of eye tracking research on multimedia learning. Computers and Education, 125,...
    • Asadi, A., Saeedpour-Parizi, M.R., Aiken, C.A., Jahanbani, Z., Houminiyan-Sharif-Abadi, D., Simpson, T., & Marchant, D. (2022). Effects...
    • Azevedo, R., & Gaševi, D. (2019). Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies:...
    • 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
    • Campbell, D.F., & Stanley, J. (2005). Diseños experimentales y cuasiexperimentales en la investigación social. Amorrortu. https://bit.ly/3JJDubN
    • Chango, W., Lara, J.A., Cerezo, R., & Romero, C. (2022). A review on data fusion in multimodal learning analytics and educational data...
    • Chemerys, H.Y., & Ponomarenko, O.V. (2022). Opportunities and prospects for personalizing the user interface of the educational platform...
    • Corp, I. (2022). SPSS Statistical Package for the Social Sciences (SPSS) (Versión 28). [Software]. IBM. https://ibm.co/3hWIls7
    • Coskun, A., & Cagiltay, K. (2022). A systematic review of eye-tracking-based research on animated multimedia learning. Journal of Computer...
    • Demšar, J., Curk, T., Erjavec, A., Gorup, C., Hoevar, T., Milutinovic, M., Možina, M., Polajnar, M., Toplak, M., Stari, A., Štajdohar, M.,...
    • Díez-Pastor, J.F., García-Osorio, C., & Rodríguez, J.J. (2014). Tree ensemble construction using a GRASP-based heuristic and annealed...
    • Diwanji, V.S. (2022). Improving accessibility and inclusiveness of university websites for international students: a mixed-methods usability...
    • Elliott, L.J., Lum, H.C., Aqlan, F., Zhao, R., & Lasher, C.D. (2020). A Study of Metacognitive Problem Solving in Undergraduate Engineering...
    • Feng, S., & Law, N. (2021). Mapping artificial intelligence in education research: A network-based keyword analysis. International Journal...
    • Fiorella, L. (2022). Learning by explaining after pauses in video lectures: Are provided visuals a scaffold or a crutch? Applied Cognitive...
    • Giannakos, M.N., Sharma, K., Pappas, I.O., Kostakos, V., & Velloso, E. (2019). Multimodal data as a means to understand the learning experience....
    • He, D., Wang, Z., Khalil, E.B., Donmez, B., Qiao, G., & Kumar, S. (2022). Classification of Driver Cognitive Load: Exploring the Benefits...
    • Joe-Louis-Paul, Sasirekha, I., Uma-Maheswari, S., Ajith, S., Arjun, K.A.M., Athesh-Kumar, S.M., & S (2019). Eye Gaze Tracking-Based Adaptive...
    • Kao, G.Y.M., Chiang, X.Z., & Foulsham, T. (2019). Reading behavior and the effect of embedded selfies in role-playing picture e-books:...
    • Kuhnel, M., Seiler, L., Honal, A., & Ifenthaler, D. (2018). Mobile learning analytics in higher education: Usability testing and evaluation...
    • Kulomäki, J., Oksama, L., Rantanen, E., & Hyönä, J. (2022). Attention control in a demanding dynamic time-sharing environment: An eye-tracking...
    • Luo, W., & Zhou, R. (2020). Can working memory task-related EEG biomarkers measure fluid intelligence and predict academic achievement...
    • Mcleod, G., Mckendrick, M., Tafili, T., Obregon, M., Neary, R., Mustafa, A., Raju, P., Kean, D., Mckendrick, G., & Mckendrick, T. (2022)....
    • Merchie, E., Heirweg, S., & Van-Keer, H. (2022). Mind maps: Processed as intuitively as thought? Investigating late elementary students’...
    • 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...
    • Molina, A.I., Navarro, Ó., Lacruz, M., & Ortega, M. (2017). El empleo de técnicas de seguimiento ocular para evaluar materiales educativos...
    • Ollesch, L., Heimbuch, S., & Bodemer, D. (2021). Improving learning and writing outcomes: Influence of cognitive and behavioral group...
    • Pi, Z., & Hong, J. (2016). Learning process and learning outcomes of video podcasts including the instructor and PPT slides: A Chinese...
    • Prokop, M., Pila, L., & Tichá, I. (2020). Impact of think-aloud on eye-tracking: A comparison of concurrent and retrospective think-aloud...
    • Rodziewicz-Cybulska, A., Krejtz, K., Duchowski, A.T., & Krejtz, I. (2022). Measuring cognitive effort with pupillary activity and fixational...
    • Rother, A., & Spliopoulou, M. (2022). Virtual reality for medical annotation tasks: A systematic review. Frontiers in Virtual Reality,...
    • Sáiz-Manzanares, M.C., Diez, J.J.R., Marticorena-Sánchez, R., Zaparaín-Yáñez, M.J., & Menéndez, R. (2020). Lifelong learning from sustainable...
    • Sáiz-Manzanares, M.C., García-Osorio, C.I., Díez-Pastor, J.F., & Martín-Antón, L.J. (2019). Will personalized e-Learning increase deep...
    • Sáiz-Manzanares, M.C., Payo-Hernanz, R.J., Zaparaín-Yáñez, M.J., Andrés-López, G., Marticorena-Sánchez, R., Calvo-Rodríguez, A., Martín, C.,...
    • Sáiz-Manzanares, M.C., Pérez, I.R., Rodríguez, A.A., Arribas, S.R., Almeida, L., & Martin, C.F. (2021a). Analysis of the learning process...
    • Scharinger, C., Schüler, A., & Gerjets, P. (2020). Using eye-tracking and EEG to study the mental processing demands during learning of...
    • Schweizer, T., Wyss, T., & Gilgen-Ammann, R. (2022). Detecting soldiers’ fatigue using eye-tracking glasses: Practical field applications...
    • Seifert, L., Cordier, R., Orth, D., Courtine, Y., & Croft, J.L. (2017). Role of route previewing strategies on climbing fluency and exploratory...
    • 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.,...
    • Shojaeizadeh, M., Djamasbi, S., Paffenroth, R.C., & Trapp, A.C. (2019). Detecting task demand via an eye tracking machine learning system....
    • Souchet, A.D., Philippe, S., Lourdeaux, D., & Leroy, L. (2022). Measuring Visual Fatigue and Cognitive Load via Eye Tracking while Learning...
    • Taub, M., & Azevedo, R. (2019). How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes...
    • Taub, M., Mudrick, N.V., Azevedo, R., Millar, G.C., Rowe, J., & Lester, J. (2017). Using multi-channel data with multi-level modeling...
    • Thees, M., Altmeyer, K., Kapp, S., Rexigel, E., Beil, F., Klein, P., Malone, S., Brünken, R., & Kuhn, J. (2022). Augmented reality for...
    • Tobii Pro Lab (Ed.) (2023). User Manual v.1.194. https://bit.ly/41KjHQm
    • Tsai, M.J., Wu, A.H., Bråten, I., & Wang, C.Y. (2022). What do critical reading strategies look like? Eye-tracking and lag sequential...
    • Van-Marlen, T., Van Wermeskerken, M., Jarodzka, H., & Van-Gog, T. (2018). Effectiveness of eye movement modeling examples in problem solving:...
    • Yang, F.Y., & Wang, H.Y. (2023). Tracking visual attention during learning of complex science concepts with augmented 3D visualizations....
Fundación Dialnet

Mi Documat

Opciones de artículo

Opciones de compartir

Opciones de entorno