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An Approach for an Affective Educational Recommendation Model

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Recommender Systems for Technology Enhanced Learning

Abstract

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.

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Notes

  1. 1.

    APML: http://apml.areyoupayingattention.com/

  2. 2.

    CAM: https://sites.google.com/site/camschema/home

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Acknowledgments

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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Authors and Affiliations

  1. aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, C/Juan del Rosal, 16, Madrid, 28040, Spain

    Olga C. Santos, Jesus G. Boticario & Ángeles Manjarrés-Riesco

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  1. Olga C. Santos
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  2. Jesus G. Boticario
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  3. Ángeles Manjarrés-Riesco
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Corresponding author

Correspondence to Olga C. Santos .

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Editors and Affiliations

  1. Agro-Know, Athens, Greece

    Nikos Manouselis

  2. Faculty of Psychology and Educational Sciences, Welten Institute – Research Centre for Learning, Teaching and Technology, Open University of the Netherlands, Heerlen, The Netherlands

    Hendrik Drachsler

  3. Department of Computer Science, VUB & KU Leuven, Leuven, Belgium

    Katrien Verbert

  4. aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, Madrid, Spain

    Olga C. Santos

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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

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  • DOI: https://doi.org/10.1007/978-1-4939-0530-0_6

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