Supporting the Transition from Block-Based to Text-Based Programming in a Non-English-Speaking Context Using the LogicFun Platform

This mixed-methods case study investigated the transition from block-based to text-based programming among Greek middle school students. Twenty students (ages 12–15) participated in a ten-week program using LogicFun, a client-side web platform integrating native language tutorials, visual blocks, and real-time Python code generation via the Pyodide library. The data included pre/post questionnaires, field notes, reflective journals, and programming artifacts. The results demonstrated statistically significant improvements in Python understanding (p < 0.001, d = 1.76) and programming confidence (p < 0.01, d = 0.78). Thematic analysis revealed four key themes: dual representation facilitating syntax comprehension, native language support reducing cognitive load, progressive development of computational thinking, and the challenge of debugging. This study contributes empirical evidence on dual-modality programming environments, highlighting the critical role of linguistic accessibility in programming education for non-English-speaking contexts by demonstrating its significant impact on cognitive load and student confidence, with broader implications for the design of inclusive programming tools for linguistically diverse learners.