Affiliations: Department of Computer Science, ETH Zurich, Zurich, Switzerland. kaesert@inf.ethz.ch | Department of Computer Science, ETH Zurich, Zurich, Switzerland | Department of Computer Science, ETH Zurich, Zurich, Switzerland; Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland
Abstract: In this paper, we explore the possibility of a general framework for modelling engagement dynamics in software tutoring, focusing on the cases of developmental dyslexia and developmental dyscalculia. This project aims at capturing the similar engagement state patterns for the two learning disabilities. We start by presenting a model of engagement dynamics in spelling learning, which relates input behaviour to learning and explains the dynamics of engagement states. Predictive power of extracted features is increased by incorporating domain knowledge in the pre-processing. The introduced model enables the prediction of focused and receptive states, and of forgetting. In the second part, we extend the model to a more general framework, which takes into account the similarities and dissimilarities of the two studied cases. Finally, we define desirable properties of a general engagement dynamics model, while analysing the reusability of the introduced spelling model.
