Abstract
Emotions and learning are closely related. In the PhD research presented in this paper, that relation has to be taken advantage of. With this aim, within the framework of affective computing, the main goal proposed is modeling learner’s affective state in order to support adaptive features and provide an inclusive personalized e-learning experience. At the first stage of this research, emotion detection is the principal issue to cope with. A multimodal approach has been proposed, so gathering data from diverse sources to feed data mining systems able to supply emotional information is being the current ongoing work. On the next stages, the results of these data mining systems will be used to enhance learner models and based on these, offer a better e-learning experience to improve learner’s results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carro, R.M.: Applications of adaptive hypermedia in education. Computers and Education, 1–12 (2008)
Boticario, J.G., Santos, O.C.: An open IMS-based user modelling approach for developing adaptive learning management systems. Journal of Interactive Media in Education 2 (2007)
Tao, J., Tan, T.: Affective computing: A review. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, pp. 981–995. Springer, Heidelberg (2005)
Ritter, J.K.: On the Affective Challenges of Developing a Pedagogy of Teacher Education. Studying Teacher Education 7, 219–233 (2011)
Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 39–58 (January 2009)
Epp, C., Lippold, M., Mandryk, R.L.: Identifying emotional states using keystroke dynamics. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, pp. 715–724 (2011)
Carberry, S., de Rosis, F.: Introduction to special Issue on “Affective modeling and adaptation”. User Modeling and User-Adapted Interaction 18, 1–9 (2008)
Shen, L., Wang, M., Shen, R.: Affective e-learning: Using “emotional” data to improve learning in pervasive learning environment. Educational Technology & Society 12, 176–189 (2009)
Santos, O.C., Boticario, J.G.: Affective Issues in Semantic Educational Recommender Systems. In: CEUR workshop proceedings, vol. 896, pp. 71–82 (2012)
Santos, O.C., Salmeron-Majadas, S., Boticario, J.G.: Emotions detection from math exercises by combining several data sources. In: Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013) (in press, 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salmeron-Majadas, S., Santos, O.C., Boticario, J.G. (2013). Inclusive Personalized e-Learning Based on Affective Adaptive Support. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-38844-6_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38843-9
Online ISBN: 978-3-642-38844-6
eBook Packages: Computer ScienceComputer Science (R0)