Automatically measuring the effect of strategy drawing features on pupils' handwriting and gender

Children's dynamic drawing strategies have been recently recognized as indicators of handwriting ability. However the influence of each feature in predicting handwriting is unknown due to lack of a measuring system. An automated measuring algorithm suitable for psychological assessment and non-subjective scoring is presented here. Using the weight vector and classification rate of a machine learning algorithm, an overall feature's effect is calculated which is comparable in different groupings. In this study thirteen previously detected drawing strategy features are measured for their influence on handwriting and gender. Features are extracted from drawing a triangle, Beery VMI and Bender Gestalt tangent patterns. Samples are related to 203 pupils (77 below average writers, and 101 female). The results show that the number of strokes in drawing the triangle pattern plays a major role in both groupings; however Left Tendency flag feature is affected by children's handwriting about 2.5 times greater than their gender. Experiments indicate that different forms of a feature sometimes show different influences.