Transforming Children's Python Turtle Graphics Learning with LLM Technology: A Design Proposal
Mondheera Pituxcoosuvarn, Yohei Murakami · 2024
STEM education, particularly programming and coding, is of great importance in today's technological landscape. Turtle graphics, an effective tool for teaching programming concepts to children, is widely used in languages such as Python, known for its simplicity and readability. However, coding can be challenging for young learners, necessitating individualized support from teachers. Large language models (LLMs), which are already employed in debugging, present an opportunity to enhance educational support systems by providing personalized hints without revealing answers, thus preserving the educational value. This proposal aims to explore the use of LLMs to generate tailored hints and explanations for different age groups and skill levels, creating a dynamic and responsive learning environment. Additionally, the proposed system includes task creation that adapts to the student's previous performance and completed tasks, ensuring continuous and appropriately challenging learning experiences. The goal of our research is to design a support system that leverages LLM technology to improve children and young students' learning in Python Turtle graphics. This system promises personalized educational support and adaptive task generation, enhancing the overall learning experience for young programmers. Future studies are necessary to test this system with real users, evaluate its effectiveness, and refine its design based on practical feedback.