Spatial construction—creating novel spatial arrangements or copying existing ones—is a hallmark of human spatial cognition. Spatial construction appears early in development, predicts later spatial and mathematical skills, and is used throughout life. This project focuses on block building, an accessible and adaptable skill for young children in both formal and informal learning contexts. We use the block-building process as a window to understand how this complex spatial skill develops, how it is linked to academic learning more generally, and how it can be nurtured, moving children from “novice” to “expert” builders. More specifically, we use traditional cognitive methods along with machine learning techniques such as computer vision and inertial measurement units embedded in the blocks to characterize in detail the process of children’s block building and its development. This innovative approach not only provides a better understanding of early spatial skills but also has the potential to change the conceptual framework through
which we understand block building by establishing novel metrics for performance. We aim to characterize the detailed processes of block
building among children of different ages and levels of experience, ranging from gifted/talented children to children with spatial impairments, broadening the potential implications for education. This project will also work toward generating scalable tools that can be used by scientists and educators to characterize, analyze, and promote development of block-building skills.

Gregory Hager, the Mandell Bellmore Professor of computer science, and Sanjeev Khudanpur, associate professor of electrical and computer engineering, contributed to the research.

Read more at the Johns Hopkins School of Education website.