Modeling Engagement Dynamics in Spelling Learning


Gian-Marco Baschera, Alberto Giovanni Busetto, Severin Klingler, Joachim Buhmann and Markus Gross

Paper type: 
Full paper
6. 13:30-15:00, Thursday 30 June


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.


engagement modeling, feature processing, domain knowledge, dynamic Bayesian network, learning, spelling