Fostering Computational Thinking in Elementary Mathematics Instruction and Learning with the Support of Large Language Models

Abstract

Computational thinking (CT) integration in elementary mathematics engages young learners in the decomposition of complex problems and the construction of iterative approaches to mathematical thinking. To effectively integrate MATH+CT, we need to build teachers' capacity in developing knowledge of CT concepts and creating Math+CT activities. This can positively influence students' mathematical outcomes and their readiness for computer science (CS) in middle and high school. Although various professional development programs aim to build teachers' CT knowledge, limited research exists on how teachers apply this knowledge to classroom-based Math+CT activities. Simultaneously, the rapid improvement of large Language Models (LLMs) creates a catalyst for building an innovative resource space to support elementary teachers' integration of MATH+CT in their existing school or district curriculum. In this poster, we present a tool that leverages LLMs to support teachers in creating Scratch programs designed to explore and deepen students' understanding of mathematical concepts.