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.

这项研究着眼于儿童绘画策略，这些策略侧重于使儿童产生锯齿状对象的笔划顺序和顺序。根据6组逻辑结构检查了绘制策略；（1）嵌入；（2）堆积物堆积；（3）预期的嵌入；（4）预期堆叠；（5）部分取景；（6）全帧化。过去的工作研​​究了纸上图纸的这些逻辑结构，并使用传统的观察方法进行评估。这种传统方法是一种详尽的方法，由于数据量过多而导致人为错误，因此会导致准确性不足。为了解决这个问题，我们扩展了触摸屏上的绘画工作，在该屏幕上，儿童的绘画数据使用新型的深度学习混合模型（LTSM中的Levenshtein Distance优化的模糊字符串匹配-FLSTM）对绘制的策略进行分类。我们开发了一个具有8个锯齿对象的触摸绘图应用程序作为绘图任务。参与研究的32位5至12岁的儿童共收集了420张图画。在提议的新颖模型与现有模型（例如长短期记忆模型（LSTM），卷积神经网络模型（CNN）和Fuzzy-CNN模型）之间进行了比较模型性能的比较，以比较图纸的分类精度。结果表明，提出的新型深度学习混合模型优于其他模型，其精确度得分为89.1％，召回率为88.6％，F1得分为88.6％。在提出的深度学习模型的帮助下，我们能够通过发达的儿童绘画系统探索和了解有关人类心理行为的更多信息。