2026-04-18T22:14:43Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/109062025-10-01T00:05:31Zcom_10259_4219com_10259_5086com_10259_2604col_10259_7349

Detection of Stress Stimuli in Learning Contexts of iVR Environments Ramírez Sanz, José Miguel Peña-Alonso, Helia Marina Serrano Mamolar, Ana Arnaiz González, Álvar Bustillo Iglesias, Andrés Machine learning Semi-supervised learning Inmersive virtual reality Game-based learning Eye-tracking Stress Inteligencia artificial en la enseñanza Aprendizaje automático Artificial intelligence-Educational applications Machine learning Comunicación presentada en: International Conference on Extended Reality, XR Salento 2023, held in Lecce, Italy during September 6–9, 2023 The use of eye-tracking in immersive Virtual Reality (iVR) is becoming an important tool for improving the learning outcomes. Nevertheless, the best Machine Learning (ML) technologies for the exploitation of eye-tracking data is yet unclear. Actually, one of the main drawbacks of some ML technologies, such as classifiers, is the scarce labeled data for training models, being the process of data annotation time-consuming and expensive. This paper presents a complete experimentation where different ML algorithms were tested, both supervised and semi-supervised, for trying to identify the stressors/distractors present in iVR learning experiences simulating the operation of a bridge crane. Results shown that the use of semi-supervised techniques can improve the performance of the Machine Learning methods making possible the identification of stressful situations in iVR environments. The use of semi-supervised learning techniques makes possible training ML algorithms without the need of great amount of labeled data which makes the data exploitation cheaper and easier. This work was supported by the Junta de Castilla y León under project BU055P20 (JCyL/FEDER, UE), the Ministry of Science and Innovation of Spain under project PID2020-119894GB-I00, co-financed through European Union FEDER funds. This work is part of the project Humanaid (TED2021-129485B-C43) funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR. We also acknowledge European Union NextGenerationEU/PRTR funds for the Margarita Salas 2022–2024 Grant awarded by Universidad de Burgos. It also was supported through the Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/875/2021). 2025-09-30T09:58:17Z 2025-09-30T09:58:17Z 2023-09-05 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion 978-3-031-43404-4 978-3-031-43403-7 https://hdl.handle.net/10259/10906 10.1007/978-3-031-43404-4_29 eng Extended Reality: XR Salento 2023, Proceedings, Part II, V. 14219, p. 427–440 https://doi.org/10.1007/978-3-031-43404-4_29 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-129485B-C43/ES/Sistemas dinámicos inteligentes centrados en el usuario para la Prevención de Riesgos Laborales/ info:eu-repo/semantics/openAccess application/pdf Springer https://riubu.ubu.es/bitstream/10259/10906/3/Ramirez-Detection_Stress_Stimuli_Learning_Contexts_iVR_Environments_2023.pdf.jpg Hispana TEXT http://rightsstatements.org/vocab/CNE/1.0/ RIUBU. Repositorio Institucional de la Universidad de Burgos https://hdl.handle.net/10259/10906