Abstract
A method for recognizing or estimating learners’ affective state plays a key role for realizing agent-based conversational e-Learning. In this paper, we focus on the learners’ interest level as an example of the important affective state, and investigate a method for estimating it from their nonverbal behaviors. In conversational situations, the sense of the nonverbal behaviors will vary depending on the contexts of the conversations. Therefore we do not use the nonverbal behaviors themselves but use the occurrence frequencies of the nonverbal behaviors as inputs for estimation mechanism. In the result of our experiment, the proposed method could estimate whether the learners’ interest level is “High” or “Low” with the accuracy of more than 70%.
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
Xenos, M., Pierrakeas, C., Pintelas, P.: A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the hellenic open university. Computers and Education 39(4), 361–377 (2002)
Danchak, M.M.: Bringing Affective Behavior to e-Learning. The Technology Source (September/October 2002)
Matsuura, K., Ogata, H., Yano, Y.: Supporting Asynchronous Communication in an Agent-based Virtual Classroom. Int’l Journal of Continuing Engineering Education and Lifelong Learning 12(5-6), 433–447 (2002)
Mahmood, A.K., Ferneley, E.: Emboddied Agents in E-Learning Environments: An Exploratory Case Study. Journal of Interactive Learning Research 17(2), 143–162 (2006)
Neji, M., Ammar, M.B.: Agent-based Collaborative Affective e-Learning Framework. The Electronic Journal of e-Learning 5(2), 123–134 (2007)
Mota, S., Picard, R.W.: Automated posture analysis for detecting learner’s interest level. In: Proc. of the Conf. on CVPR Workshop, p. 49 (2003)
Nosu, K., Kurokawa, T.: A multi-modal emotion diagnosis system to support e-learning. In: Proc. of the 1st Int’l Conf. on Innovative Computing, Information and Control, vol. 2, pp. 274–278 (2006)
Fujisawa, K., Aihara, K.: Estimation of user interest from face approaches captured by webcam. In: Shumaker, R. (ed.) VMR 2009. LNCS, vol. 5622, pp. 51–59. Springer, Heidelberg (2009)
Morris, D.: Bodytalk: The Meaning of Human Gestures. Crown Publishers (1995)
Peters, C., Pelachaud, C., Bevacqua, E., Mancini, M., Poggi, I.: A Model of Attention and Interest Using Gaze Behavior. In: Panayiotopoulos, T., Gratch, J., Aylett, R.S., Ballin, D., Olivier, P., Rist, T. (eds.) IVA 2005. LNCS (LNAI), vol. 3661, pp. 229–240. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nakamura, K., Kakusho, K., Shoji, T., Minoh, M. (2012). Investigation of a Method to Estimate Learners’ Interest Level for Agent-Based Conversational e-Learning. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31715-6_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-31715-6_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31714-9
Online ISBN: 978-3-642-31715-6
eBook Packages: Computer ScienceComputer Science (R0)