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2014-01-01
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info:eu-repo/semantics/openAccess
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Resumen
There is agreement in the literature that affect influences learning. In turn, addressing affective issues in the recommendation process has shown their ability to increase the performance of recommender systems in non-educational scenarios. In our work, we combine both research lines and describe the SAERS approach to model affective educational recommendations. This affective recommendation model has been initially validated with the application of the TORMES methodology to specific educational settings. We report 29 recommendations elicited in 12 scenarios by applying this methodology. Moreover, a UML formalized version of the recommendations model which can describe the recommendations elicited is presented in the paper.
Descripción
This version of the chapter has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-1-4939-0530-0_6
Authors would like to thank the European Commission and the Spanish Government for funding the projects of aDeNu research group that have supported this research work. In particular, MAMIPEC (TIN2011-29221-C03-01), A2UN@ (TIN2008-06862-C04-01/TSI) and EU4ALL (FP6-2005-IST-5). Moreover, they would also like to thank the educators who were involved in the recommendations elicitation process, as well as in the corresponding evaluation.
Authors would like to thank the European Commission and the Spanish Government for funding the projects of aDeNu research group that have supported this research work. In particular, MAMIPEC (TIN2011-29221-C03-01), A2UN@ (TIN2008-06862-C04-01/TSI) and EU4ALL (FP6-2005-IST-5). Moreover, they would also like to thank the educators who were involved in the recommendations elicitation process, as well as in the corresponding evaluation.
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Santos, O.C., Boticario, J.G., Manjarrés-Riesco, Á. (2014). An Approach for an Affective Educational Recommendation Model. In: Manouselis, N., Drachsler, H., Verbert, K., Santos, O. (eds) Recommender Systems for Technology Enhanced Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0530-0_6
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial