Abstract
Standard Natural Classroom (SNC) is a real-time classroom based on smart space and design principles of e-learning, aiming at creating face-to-face, interactive and pervasive learning scene for students who are far from live classroom. We use various techniques in developing different kinds of components in SNC. Two components among them are specially described in this paper: E-pen and Emotion Understanding. E-pen focuses on helping teachers mark on the projection screen and several recognition algorithms are mentioned, while Emotion Understanding focuses on affective learning and is used to estimate students’ emotion.
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
Picard, R.W., Papert, S., Bender, W., et al.: Affective Learning - manifesto. BT Technology Journal 22(4) (October 2004)
Shen, R., Yang, F., Han, P.: An open learning model for web-based learning architecture. Web-Based Learning Theory, Research, and Practice, Shanghai, China (2006)
Le Cun, Y., Boser, B., Denker, J.S., et al.: Handwritten Digit Recognition with a Back-Propagation Network. In: Advances in neural information processing systems vol. 2 (1990)
Bottou, L., Cortes, C., Denker, J.S., Drucker, H., et al.: Comparison of Classifier Methods: A Case Study in Handwritten Digit Recognition. In: The International Conference on Pattern Recognition, Jerusalem, Israel, pp. 77–82 (October 1994)
Júnior, J.J.O., de C. Carvalho, J.M., Freitas, C.O.A., Sabourin, R.: Evaluating NN and HMM Classifiers for Handwritten Word Recognition. In: The XV Brazilian Symposium on Computer Graphics and Image Processing, pp. 210–217. IEEE, Los Alamitos (2002)
de Oliveira, J.J., de Carvalho Jr., J.M., de A Freitas, C.O., Sabourin, R.: Feature Sets Evaluation for Handwritten Word Recognition. Frontiers in Handwriting Recognition (2002)
Heraz, A., Razaki, R., Frasson, C.: Using machine learning to predict learner emotional state from brainwaves. In: Proceedings of ICALT, Japan (2007)
Shen, L., Leon, E., Callaghan, V., Shen, R.: Exploratory Research on an Affective E-Learning Model. In: Proceedings of Workshop on Blended Learning (2007)
Savran, A., et al.: Emotion Detection in the Loop from Brain Signals and Facial Images. In: Proceedings of eNTERFACE 2006, Dubrovnik (2006)
Reynolds, C.J.: The Sensing and Measurement of Frustration with Computers. Master thesis, Massachusetts Institute of Technology
Russell, J.A.: A Circumplex Model of Affect. Journal of Personality and Social Psychology 39(6), 1161–1178 (1980)
Lepper, M.R., Chabay, R.W.: Socialising the intelligent tutor: bringing empathy to computer tutors. In: Mandl, H., Lesgold, A. (eds.) Learning issues for intelligent tutoring systems, pp. 242–257 (1988)
Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques (1991), ISBN 0-8186-8930-7
Chang, C.-C., Lin, C.-J.: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Picard, R.W., Scheirer, J.: The Galvactivator: a glove that senses and communicates skin conductivity. In: 9th ICHCI, New Orleans (August 2001)
Thought Technology Ltd. Canada, http://www.thoughttechnology.com
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, C., Zhou, J., Shen, L., Shen, R. (2008). Techniques for Enhancing Pervasive Learning in Standard Natural Classroom. In: Fong, J., Kwan, R., Wang, F.L. (eds) Hybrid Learning and Education. ICHL 2008. Lecture Notes in Computer Science, vol 5169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85170-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-85170-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85169-1
Online ISBN: 978-3-540-85170-7
eBook Packages: Computer ScienceComputer Science (R0)