A Bayesian Network Model for Discovering Handwriting Strategies of Primary School Children

In this paper, we present the results of a longitudinal study for the evolution follow-up in writing among typical pupils in primary education. We propose a method aimed at discovering groups of pupils sharing the same handwriting strategies along their primary education. From the on-line acquisition of writing and drawing tests, writing strategies are modeled by means of a bayesian network. Expert knowledge partially determines the bayesian network structure, in which the writing strategy is represented by a latent variable. By considering that each writing test is represented by its own (local) strategy and that there exists a global strategy which deals with each local strategy, we propose a Global Hierarchical Model. This model is an unsupervised classifier that links handwriting and drawing features, local and global strategies. We give here preliminary results concerning a first phase of strategy discovering and labelling, and a second phase of observation of the pupil evolution in the discovered strategies.