Fecha
2013
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info:eu-repo/semantics/closedAccess
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Springer Nature
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0 citas en 
2 citas en 
Resumen
Emotions and learning are closely related. In the PhD research presented in this paper, that relation has to be taken advantage of. With this aim, within the framework of affective computing, the main goal proposed is modeling learner’s affective state in order to support adaptive features and provide an inclusive personalized e-learning experience. At the first stage of this research, emotion detection is the principal issue to cope with. A multimodal approach has been proposed, so gathering data from diverse sources to feed data mining systems able to supply emotional information is being the current ongoing work. On the next stages, the results of these data mining systems will be used to enhance learner models and based on these, offer a better e-learning experience to improve learner’s results.
Descripción
The registered version of this conference paper, first published in "Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg", is available online at the publisher's website: https://doi.org/10.1007/978-3-642-38844-6_45
Categorías UNESCO
Palabras clave
Affective computing, Emotions, User Modeling, Human-Computer Interaction, Data Mining, Artificial Intelligence, Multimodal Approach
Citación
Salmeron-Majadas, S., Santos, O.C., Boticario, J.G. (2013). Inclusive Personalized e-Learning Based on Affective Adaptive Support. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_45
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial