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2017-07-09
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info:eu-repo/semantics/closedAccess
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Resumen
Recent research has provided solid evidence that emotions strongly affect motivation and engagement, and hence play an important role in learning. In BIG-AFF project, we build on the hypothesis that ``it is possible to provide learners with a personalised support that enriches their learning process and experience by using low intrusive (and low cost) devices to capture affective multimodal data that include cognitive, behavioural and physiological information''. In order to deal with the affect management complete cycle, thus covering affect detection, modelling and feedback, there is lack of standards and consolidated methodologies. Being our goal to develop realistic affect-aware learning environments, we are exploring different approaches on how these can be supported by either by traditional non-intrusive interaction sources or low intrusive and inexpensive sensing devices. In this work we describe the main issues involved in two user studies carried out with high school learners, highlight some open problems that arose when designing the corresponding experimental settings. In particular, the studies involved varied nature of information sources and each focused on one of the approaches. Our experience reflects the need to develop an extensive knowledge about the organization of this type of experiences that consider user-centric development and evaluation methodologies.
Descripción
The registered version of this conference paper, first published in "UMAP 2017 - Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, 287-292.", is available online at the publisher's website: https://doi.org/10.1145/3099023.3099084
Categorías UNESCO
Palabras clave
Affective computing, Design and evaluation methods, Labelling user interactions, Learner Modeling, Data Mining
Citación
Jesus G. Boticario, Olga C. Santos, Raúl Cabestrero, Pilar Quirós, Sergio Salmerón-Majadas, Raúl Uria-Rivas, Mar Saneiro, Miguel Arevalillo-Herráez, and Francesc J. Ferri. 2017. BIG-AFF: Exploring Low Cost and Low Intrusive Infrastructures for Affective Computing in Secondary Schools. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP '17). Association for Computing Machinery, New York, NY, USA, 287–292. https://doi.org/10.1145/3099023.3099084
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial