Classification of children's drawing strategies on touch-screen of seriation objects using a novel deep learning hybrid model

This research looks into children's drawing strategies that focus on sequencing and order of strokes for children to produce a seriation object. The drawing strategies were examined according to 6 sets of logical structures that are; (1) embedding; (2) accretion stacking; (3) anticipated embedding; (4) anticipated stacking; (5) partial framing; and (6) full framing. Past work studied these logical structures for drawings on paper and used the traditional method of observation for evaluation. This traditional method is an exhaustive approach and leads to in-accuracies due to human error as a result of ambigous data. To solve this, we extend the work for drawings on touch screen where children's drawing data were quantified using a novel deep learning hybrid model (Fuzzy string matching optimized with Levenshtein Distance in LTSM - FLSTM) to classify the drawn strategies. We developed a touch drawing application with 8 seriation objects as the drawing task. 32 children of age between 5 and 12 years old took part in this study with a total of 420 drawings collected. Comparative model performance was done between the proposed novel model with existing models such as Long Short-term Memory model (LSTM), Convolution Neural Network model (CNN) and Fuzzy-CNN model for comparison in drawing classification accuracy. The results showed that the proposed novel deep learning hybrid model outperformed other models with a precision score of 89.1, recall of 88.6 and F1 score of 88.6. With assistance of the proposed deep learning model, we were able to explore and understand more about human psychological behaviour through the developed children drawing system. © 2020 Faculty of Engineering, Alexandria University