Abstract
In this paper, we introduce a model of engagement dynamics in spelling learning. The model relates input behavior to learning, and explains the dynamics of engagement states. By systematically incorporating domain knowledge in the preprocessing of the extracted input behavior, the predictive power of the features is significantly increased. The model structure is the dynamic Bayesian network inferred from student input data: an extensive dataset with more than 150 000 complete inputs recorded through a training software for spelling. By quantitatively relating input behavior and learning, our model enables a prediction of focused and receptive states, as well as of forgetting.
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.
Similar content being viewed by others
References
Cooper, D.G., Muldner, K., Arroyo, I., Woolf, B.P., Burleson, W.: Ranking feature sets for emotion models used in classroom based intelligent tutoring systems. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 135–146. Springer, Heidelberg (2010)
Heray, A., Frasson, C.: Predicting Learner Answers Correctness through Brainwaves Assessment and Emotional Dimensions. In: AIED 2009, pp. 49–56 (2009)
Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting student misuse of intelligent tutoring systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531–540. Springer, Heidelberg (2005)
Johns, J., Woolf, B.: A Dynamic Mixture Model to Detect Student Motivation and Proficiency. In: UMAP 2006, pp. 163–168 (2006)
Busetto, A.G., Ong, C.S., Buhmann, J.M.: Optimized Expected Information Gain for Nonlinear Dynamical Systems. In: ICML 2009, pp. 97–104 (2009)
Arroyo, I., Woolf, B.: Inferring Learning and Attitudes from a Bayesian Network of Log File Data. In: AIED 2005, pp. 33–40 (2005)
Kort, B., Reilly, R., Picard, R.W.: An Affective Model of Interplay Between Emotions and Learning: Reengineering Educational Pedagogy - Building a Learning Companion. Advanced Learning Technologies, 43–46 (2001)
Gross, M., Vögeli, C.: A Multimedia Framework for Effective Language Training. Computer & Graphics 31, 761–777 (2007)
Kast, M., Meyer, M., Vögeli, C., Gross, M., Jäncke, L.: Computer-based Multisensory Learning in Children with Developmental Dyslexia. Restorative Neurology and Neuroscience 25(3-4), 355–369 (2007)
Kullback, S., Leibler, R.A.: On Information and Sufficiency. Annals of Mathematical Statistics 22(1), 79–86 (1951)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Baschera, G.M., Gross, M.: Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training. International Journal of AIED 20(1) (2010) (in press)
Nelder, J.A., Mead, R.: A Simplex Method for Function Minimization. Computer Journal 7, 308–313 (1965)
Murphy, K.: The Bayes Net Toolbox for Matlab. Computing Science and Statistics 33 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baschera, GM., Busetto, A.G., Klingler, S., Buhmann, J.M., Gross, M. (2011). Modeling Engagement Dynamics in Spelling Learning. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science(), vol 6738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21869-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-21869-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21868-2
Online ISBN: 978-3-642-21869-9
eBook Packages: Computer ScienceComputer Science (R0)