Context: Schools, training platforms, and technology firms increasingly need to assess programming proficiency at scale with transparent, reproducible methods that support personalized learning pathways. Objective: This study introduces a pedagogical framework for Scratch project assessment, aligned with the Common European Framework of Reference (CEFR), providing universal competency levels for students and teachers alongside actionable insights for curriculum design. Method: We apply Fuzzy C-Means clustering to 2008246 Scratch projects evaluated via Dr.Scratch, implementing an ordinal criterion to map clusters to CEFR levels (A1-C2), and introducing enhanced classification metrics that identify transitional learners, enable continuous progress tracking, and quantify classification certainty to balance automated feedback with instructor review. Impact: The framework enables diagnosis of systemic curriculum gaps-notably a "B2 bottleneck" where only 13.3% of learners reside due to the cognitive load of integrating Logic Synchronization, and Data Representation--while providing certainty--based triggers for human intervention.

翻译：背景：学校、培训平台及技术公司日益需要以透明、可重复的方法大规模评估编程能力，以支持个性化学习路径。目标：本研究提出一种面向Scratch项目评估的教学框架，该框架与欧洲共同语言参考标准（CEFR）对齐，为学生和教师提供通用能力等级，并为课程设计提供可操作见解。方法：我们采用模糊C均值聚类对经Dr.Scratch评估的2008246个Scratch项目进行分析，通过引入序数准则将聚类结果映射至CEFR等级（A1-C2），并提出增强型分类指标以识别过渡型学习者、实现持续进度追踪，以及量化分类确定性以平衡自动反馈与教师评审。影响：该框架可诊断系统性课程缺口——尤其表现为“B2瓶颈”现象，即仅13.3%的学习者停留在此阶段，其成因在于逻辑同步与数据表示整合所需的认知负荷——同时基于确定性指标触发人工干预机制。