In this paper, we present a framework that explains the scores of sketches by learning rules used in developmental tests. To achieve this, we propose a deep neural network model that considers a target and the corresponding sketch images as inputs. The proposed method is divided into plain and residual models according to the presence of residual connections to compare their performance. In addition, each model includes the subtraction and concatenation approaches to fuse two feature maps. To verify the performance of the proposed method, we conduct experiments over all settings that combine the proposed models with the fusion method. The results show that the proposed framework can be used for visual-motor integration analysis by determining scores and providing explanations.