Keywords

Neuro-education, learning patterns, eye tracking, personalized learning, cluster analysis, educational technology

Abstract

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.

View infography

References

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

Crossmark

Technical information

Received: 10-12-2022

Revised: 11-01-2023

Accepted: 23-02-2023

OnlineFirst: 30-05-2023

Publication date: 01-07-2023

Article revision time: 32 days | Average time revision issue 76: -6 days

Article acceptance time: 75 days | Average time of acceptance issue 76: 72 days

Preprint editing time: 158 days | Average editing time preprint issue 76: 155 days

Article editing time: 203 days | Average editing time issue 76: 200 days

Metrics

Metrics of this article

Views: 41710

Abstract readings: 40699

PDF downloads: 1011

Full metrics of Comunicar 76

Views: 485998

Abstract readings: 474340

PDF downloads: 11658

Cited by

Cites in Web of Science

Currently there are no citations to this document

Cites in Scopus

Currently there are no citations to this document

Cites in Google Scholar

Currently there are no citations to this document

Download

Alternative metrics

How to cite

Sáiz-Manzanares, M., Marticorena-Sánchez, R., Martín-Antón, L., Almeida, L., & Carbonero-Martín, M. (2023). Application and challenges of eye tracking technology in Higher Education. [Aplicación y retos de la tecnología de movimiento ocular en Educación Superior]. Comunicar, 76, 35-46. https://doi.org/10.3916/C76-2023-03

Share

           

Oxbridge Publishing House

4 White House Way

B91 1SE Sollihul United Kingdom

Administration

Editorial office

Creative Commons

This website uses cookies to obtain statistical data on the navigation of its users. If you continue to browse we consider that you accept its use. +info X