This mixed-methods study examined computational thinking (CT) development among 93 pre-high school students in a five-day AI agent creation workshop using CocoFlow, a no-code platform. Integrating pre-post assessments, behavioral logs, and interviews, we investigated CT development and how initial CT levels shape learning trajectories. Results revealed significant improvements in abstract thinking (effect size d = 0.71) and algorithmic thinking (effect size d = 0.70). Hierarchical regression identified iterative testing engagement as a predictor of self-efficacy gains (beta = 0.20, p = 0.05). Notably, students with moderate initial CT levels demonstrated substantially greater gains than both high-CT and low-CT peers, revealing an Optimal Development Zone effect (eta squared = 0.55). Qualitative analysis showed moderate-CT students exhibited adaptive expertise, while high-CT students risked over-engineering and low-CT students struggled with task decomposition. These findings challenge linear learning assumptions and provide evidence for differentiated scaffolding in CT education.

翻译：本混合方法研究考察了93名初中生在为期五天的AI代理创作工作坊中的计算思维发展情况，该工作坊使用无代码平台CocoFlow。通过整合前后测评估、行为日志和访谈，我们探究了计算思维的发展过程以及初始计算思维水平如何塑造学习轨迹。结果显示，抽象思维（效应量d=0.71）和算法思维（效应量d=0.70）均有显著提升。分层回归分析表明，迭代测试参与度是自我效能感提升的预测因子（beta=0.20，p=0.05）。值得注意的是，初始计算思维水平中等的学生比高水平或低水平同伴表现出更大的进步，揭示了“最佳发展区”效应（eta平方=0.55）。定性分析显示，中等计算思维水平的学生展现出适应性专长，而高计算思维水平的学生存在过度工程化风险，低计算思维水平的学生则在任务分解方面有困难。这些发现挑战了线性学习假设，并为计算思维教育中的差异化支架式教学提供了证据。