Fecha
2015
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Derechos de acceso
info:eu-repo/semantics/closedAccess
Título de la revista
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Editorial
Springer Nature
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Resumen
This paper introduces the work being carried out in an ongoing PhD research focused on the detection of the learners’ affective states by combining different available sources (from physiological sensors to keystroke analysis). Different data mining algorithms and data labeling techniques have been used generating 735 prediction models. Results so far show that predictive models on affective state detection from multimodal-based approaches provide better accuracy rates than single-based.
Descripción
The registered version of this conference paper, first published in "Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science, vol 9112. Springer, Cham", is available online at the publisher's website: https://doi.org/10.1007/978-3-319-19773-9_133
Categorías UNESCO
Palabras clave
Artificial intelligence, Affective computing, data mining, Human- computer interaction, Adaptive systems, User modeling, Machine learning, Multimodal approach, Sensor data
Citación
Salmeron-Majadas, S., Santos, O.C., Boticario, J.G. (2015). Towards Multimodal Affective Detection in Educational Systems Through Mining Emotional Data Sources. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_133
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial