CLUSTERING AND BAYESIAN NETWORK APPROACHES FOR DISCOVERING HANDWRITING STRATEGIES OF PRIMARY SCHOOL CHILDREN

Abstract

The aim of this paper is to assess the evolution in writing performance amongst typical pupils in primary education. More precisely, we propose ways of discovering groups of pupils sharing the same writing strategies during their primary education and methods for the temporal modeling of these pupils' writing strategies. For this purpose, online acquisition of writing and drawing tests have been performed three times during a period of one year for the same pupils under the same experimental conditions. A first approach, based on clustering, is applied to highlight clusters on a set of dynamic primitives chosen by an expert in the field of child development psychology. Results are presented by means of a comparative study between features of each group and writing tests. An analysis of within and between-strategies migration of pupils over time is also conducted to highlight pupils who change (or fail to change) their writing strategies during this period of one year. A second approach is used to model the problem by means of a probabilistic graphical model, i.e. a bayesian network. Expert knowledge partially determines the bayesian network structure, in which the writing strategy is represented by a hidden variable whose cardinality is estimated by the results of the clustering approach. 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. The results of our hierarchical model structured using real data highlight, among others, two global strategies that correspond to normo-writer pupils and more advanced normo-writers. A longitudinal and temporal study of the evolution of the pupils in these strategies shows that these two strategies are consistent.