Fecha
2014-10-10
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info:eu-repo/semantics/closedAccess
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Springer
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Resumen
One of the most challenging context features to detect when making recommendations in educational scenarios is the learner’s affective state. Usually, this feature is explicitly gathered from the learner herself through questionnaires or self-reports. In this paper, we analyze if affective recommendations can be produced with a low cost approach using the open source electronics prototyping platform Arduino together with corresponding sensors and actuators. TORMES methodology (which combines user centered design methods and data mining techniques) can support the recommendations elicitation process by identifying new recommendation opportunities in these emerging social ubiquitous networking scenarios.
Descripción
The registered version of this conference paper, first published in "Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597, Springer, Cham", is available online at the publisher's website: https://doi.org/10.1007/978-3-319-11538-2_25
Acceso al texto completo: https://rdcu.be/eDIEs
Acceso al texto completo: https://rdcu.be/eDIEs
Categorías UNESCO
Palabras clave
Contextual recommender systems, Educational recommender systems, Ubiquitous computing, Affective computing, Arduino, Sensors, Actuators, ISO 9241-210
Citación
Santos, O.C., Boticario, J.G. (2014). Exploring Arduino for Building Educational Context-Aware Recommender Systems that Deliver Affective Recommendations in Social Ubiquitous Networking Environments. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham.
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial