Although computational thinking-focused initiatives have been rapidly expanding, there is relatively little evidence-based practical guidance on how schools and individual teachers can effectively integrate computational thinking (CT) into different subject areas in elementary school classrooms. The integration of CT across multiple subject areas holds special promise for promoting transfer of learning.

The goal of this National Science Foundation-funded project is to refine and iteratively test draft tools (a framework, self-diagnostic tool, rubric, and evaluation instruments) for identifying schools’ and teachers’ readiness to integrate CT across the curriculum. Our team will collaborate with a group of geographically dispersed school principals, teachers, evaluators, and subject-matter experts to iteratively refine these draft tools. The tools will be pilot-tested in high-poverty elementary schools in two different regions to investigate whether there is promising evidence that systematic use of these resources is associated with the successful integration of CT across different subject areas.

The tools are meant to help schools clarify their definitions of CT and CT integration, articulate their visions and plans for CT implementation, and monitor the effectiveness of teacher professional development in CT instruction. Once they are improved and ready for use among a wide range of educators, the tools will be valuable and widely accessible resources for helping schools improve the likelihood of successful CT integration and better student learning outcomes.

This work is undertaken in collaboration with partners Dr. Cheri Fancsali at the Research Alliance for NYC Schools and Dr. Maya Israel of the University of Florida, and draws on draft products from the Identifying Effective Models for Integrating Computational Thinking into NYC Elementary Schools project, which is funded by the Robin Hood Learning + Technology Fund.