BlockPython: A Process-Aware Agent-Supported Platform for the Transition from Block-Based to Python Programming

2026-08-06

AI Summary

This study addresses the cognitive gap learners experience when transitioning from block-based to Python text-based programming, primarily due to the abstract nature of textual syntax. To bridge this gap, we design and implement an intelligent learning platform supporting bidirectional conversion between blocks and Python code. The platform employs a four-phase instructional workflow—task decomposition, block-based practice, coding challenges, and extended interaction—integrated with a rule-driven program execution and assessment mechanism. By synthesizing multimodal process data, including block structures, code versions, runtime outputs, and dialogue logs, the system constructs a process-aware learning support framework. Leveraging deterministic diagnosis and intelligent agents, it dynamically delivers visual feedback and personalized scaffolding to precisely identify and intervene in difficulties related to computational thinking and code expression. This work not only establishes a reusable paradigm for block-to-text programming transitions but also enables fine-grained analysis and adaptive support throughout the learning process.

Abstract

The transition from block-based to text-based programming requires learners to convert visible program structures into abstract textual expressions, which may create a cognitive gap between understanding computational concepts and expressing them in Python syntax. To support this transition, we designed and implemented BlockPython. The platform centers on bidirectional translation between blocks and Python and guides learners through four stages: Task Decomposition, Block-Based Practice, Code Challenge, and Extended Interaction. Across these stages, learners progressively establish connections among program structure, runtime behavior, and textual code. During learning, the platform continuously collects process evidence, including block artifacts, code versions, run outcomes, use of support, and dialogue. Deterministic diagnosis, program visualization, and the learning assistant use this evidence to identify different difficulties in computational understanding and Python expression. The rule-based system is responsible for program execution, objective evaluation, and stage control, while the learning assistant uses verified evidence to provide explanations, prompts, and guiding questions. This report describes the design rationale, learning workflow, and process-aware support mechanisms of BlockPython and provides a system-design reference for supporting the transition from block-based to text-based programming and for analyzing learning processes.

Problem

• block-based programming
• text-based programming
• cognitive gap
• programming transition
• Python syntax

Innovation

• bidirectional translation
• process-aware learning
• block-to-text transition
• learning assistant
• program visualization