Unleashing computational thinking: a novel approach to reforming Indonesian primary math instruction

There are two growing demands in primary mathematics education: to deepen students' mathematical thinking, and to integrate Computational Thinking (CT)—the conceptual and algorithmic foundation underpinning artificial intelligence. However, current pedagogical strategies have proven insufficient in effectively bridging both demands. To address this persistent gap, we propose an innovative instructional syntax centered on structured, metacognitive reflection across three levels: individual, peer-to-peer, and collective. The syntax organizes argumentation, coloring tasks, creative problem posing, peer interaction, and clustered collective reflection. Students perform these actions in written form, creating a traceable record that facilitates systematic analysis and enables comparisons among peers and across the whole class. This syntax facilitates dialogical learning, ensuring all students actively participate. We piloted this instructional syntax in 17 classes with 215 primary students and examined implementation fidelity using LLM-supported transcript analysis. The implementation was effective, demonstrating the feasibility of integrating this syntax into regular classroom practice. To the best of our knowledge, this analysis represents the first application of Large Language Models (LLMs) and clustering techniques to understand the implementation of such a dialogic and reflective instructional approach. The LLM-based analysis of the lesson transcriptions, focused on the instructional syntax, provides clear guidance for continuous improvement and scaling the integration of both demands.