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.
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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
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DOI: https://doi.org/10.1007/978-3-642-21869-9_7
Publisher Name: Springer, Berlin, Heidelberg
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