Santos, Olga C.González Boticario, Jesús2025-09-122025-09-122014-10-10Santos, 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.978-3-319-11538-2https://doi.org/10.1007/978-3-319-11538-2_25https://hdl.handle.net/20.500.14468/30014The 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_25Acceso al texto completo: https://rdcu.be/eDIEsOne 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.eninfo:eu-repo/semantics/closedAccess3304 Tecnología de los ordenadoresactas de congresoContextual recommender systemsEducational recommender systemsUbiquitous computingAffective computingArduinoSensorsActuatorsISO 9241-210