Abstract
Emotions influence human cognition, affecting perception and understanding of a specific situation; therefore emotions can affect positively or negatively the learning process. Currently there are few information systems that analyze users’ emotions to optimize their learning. This article proposes a model that includes users’ temporary emotions to recommend Learning Objects (LO) and deliver relevant educational materials. Three stages are set; initially, the model recognizes a user’s emotions and learning style; then a recommendation system is applied to identify relevant LOs; and finally, the presentation of this information is showed to the user. In this work we present a method that identifies the emotion of the user based on facial recognition, and the process of recommendation is presented.
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References
Picard, R.: Affective Computing. MIT Press, Cambridge (1997)
Shen, L., Wang, M., Shen, R.: Affective e-learning: using emotional data to improve learning in pervasive learning environment. J. Educ. Technol. Soc. 12(2), 176–189 (2009)
Tejedor,A.B., Andoni, C.M.: Inteligencia y Educación Emocional (2009)
Lei, J., Rao, Y., Li, Q., Quan, X., Wenyin, L.: Towards building a social emotion detection system for online news. Future Gener. Comput. Syst. 37, 438–448 (2014)
Mizhquero, K.: Análisis, Diseño e Implementación de un Sistema Adaptivo de Recomendación de Información Basado en Mashups. In: Rev. Tecnológica ESPOL (2009)
Rodríguez, P., Duque, N., Ovalle, D.A.: Multi-agent system for knowledge-based recommendation of learning objects using metadata clustering. In: Bajo, J., et al. (eds.) PAAMS 2015 Workshops. CCIS, vol. 524, pp. 356–364. Springer, Heidelberg (2015)
Rodríguez, P.A., Ovalle, D.A., Duque, N.D.: A student-centered hybrid recommender system to provide relevant learning objects from repositories. In: Zaphiris, P., Ioannou, A. (eds.) LCT 2015. LNCS, vol. 9192, pp. 291–300. Springer, Heidelberg (2015)
Winoto, P., Tang, T.: The role of user mood in movie recommendations. Expert Syst. Appl. 37(8), 6086–6092 (2010)
Watson, D., Clark, L.A.: THE PANAS-X manual for the positive and negative affect schedule - expanded form. Order A J. Theory Ordered Sets Appl. 277(6), 1–27 (1994)
Shan, M.-K., Kuo, F.-F., Chiang, M.-F., Lee, S.-Y.: Emotion-based music recommendation by affinity discovery from film music. Expert Syst. Appl. 36(4), 7666–7674 (2009)
Santos, O.C.; Saneiro, M.; Salmeron-Majadas, S.; Boticario, J.G.: A methodological approach to eliciting affective educational recommendations. In: Advanced Learning Technologies, pp. 529–533 (2014)
Felder, R.M., Spurlin, J.: Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21(1), 103–112 (2005)
Li, W., Xu, H.: Text-based emotion classification using emotion cause extraction. Expert Syst. Appl. 41(4), 1742–1749 (2014)
Sano, A., Picard, R.W.: Stress recognition using wearable sensors and mobile phones. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 671–676 (2013)
Webb, R.C., Pielak, R.M., Bastien, P., Ayers, J., Niittynen, J., Kurniawan, J., Manco, M., Lin, A., Cho, N.H., Malyrchuk, V., Balooch, G., Rogers, J.A.: Thermal transport characteristics of human skin measured in vivo using ultrathin conformal arrays of thermal sensors and actuators. PLoS ONE 10(2), e0118131 (2015)
Vekariya, V., Kulkarn, G.R.: Hybrid recommender systems: survey and experiments. In: Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), pp. 469–473 (2012)
Acknowledgements
The research reported in this paper was funded in part by the COLCIENCIAS Project entitled “RAIM: Implementación de un framework apoyado en tecnologías móviles y de realidad aumentada para entornos educativos ubicuos, adaptativos, accesibles e interactivos para todos” Universidad Nacional de Colombia, with code 111956934172.
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Méndez, N.D.D., Zapata, Á.M.P., Collazos, C.A. (2016). A Learning Object Recommendation Model with User Mood Characteristics. In: Koch, F., Koster, A., Primo, T. (eds) Social Computing in Digital Education. SOCIALEDU 2015. Communications in Computer and Information Science, vol 606. Springer, Cham. https://doi.org/10.1007/978-3-319-39672-9_4
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DOI: https://doi.org/10.1007/978-3-319-39672-9_4
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