RT info:eu-repo/semantics/conferenceObject
T1 Detection of Stress Stimuli in Learning Contexts of iVR Environments
A1 Ramírez Sanz, José Miguel
A1 Peña-Alonso, Helia Marina
A1 Serrano Mamolar, Ana
A1 Arnaiz González, Álvar
A1 Bustillo Iglesias, Andrés
K1 Machine learning
K1 Semi-supervised learning
K1 Inmersive virtual reality
K1 Game-based learning
K1 Eye-tracking
K1 Stress
K1 Inteligencia artificial en la enseñanza
K1 Artificial intelligence-Educational applications
K1 Aprendizaje automático
K1 Machine learning
AB 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.
PB Springer
SN 978-3-031-43404-4
YR 2023
FD 2023-09-05
LK https://hdl.handle.net/10259/10906
UL https://hdl.handle.net/10259/10906
LA eng
NO Comunicación presentada en: International Conference on Extended Reality, XR Salento 2023, held in Lecce, Italy during September 6–9, 2023
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 11-may-2026