2026-10-30T14:28:32Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/120742026-09-11T00:05:35Zcom_10259_5841com_10259_5086com_10259_2604col_10259_5842

Sáiz Manzanares, María Consuelo Toribio Casado, Celia Pérez López de Echazarreta, Gloria Águeda Miguel, Rocío González Díez, Irene Muñoz Arranz, Marina Carbonero Martín, Miguel Ángel Martín Antón, Luis Jorge 2026-09-10T12:06:51Z 2026-09-10T12:06:51Z 2026-07 0144-929X https://hdl.handle.net/10259/12074 10.1080/0144929X.2026.2692095 1362-3001 Integrating multimodal devices (eye tracking, electroencephalography [EEG], and galvanic skin response [GSR]) enables the objective assessment of cognitive and emotional processes during controlled tasks. In higher education multimedia contexts, such measures can inform the adaptive design of educational interfaces. This study examined the processing of avatar images expressing positive (surprise, joy) and negative (sadness, fear) emotions across different user groups (final-year university students, novice university lecturers, and experienced university lecturers), while also considering gender effects. Forty-eight participants completed a visualisation task while physiological indicators derived from EEG (engagement, memorisation, valence, workload), GSR (impact), and eye tracking (number of visits and dwell time) were recorded for relevant and non-relevant areas of interest. Significant differences emerged for surprise, with students showing more visits to non-relevant areas than lecturers members. For joy, final-year university students exhibited longer dwell times on relevant areas than experienced lecturers. No differences by professional group were found for sadness or fear. Regarding gender, women showed higher valence and workload in the joy condition, as well as higher memorisation and longer dwell times in the sadness condition. Based on these findings, adaptive adjustments to avatar design are proposed to better align with users’ cognitive and emotional profiles. This work was supported by the State Investigation Agency. Ministry of Science, Innovation and Universities. Government of Spain [grant numbers PID2020-117111RB-I00. Project SmartLearnUni]. application/pdf eng Taylor and Francis Behaviour & Information Technology. 2016 https://doi.org/10.1080/0144929X.2026.2692095 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Physiological measures Cognitive load Higher education Expresión facial Tecnología educativa Facial expression Educational technology Multichannel assessment of cognitive load and emotions with pedagogical avatars in higher education info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Behaviour & Information Technology 1 21