# A CEFR-Inspired Classification Framework with Fuzzy C-Means To Automate Assessment of Programming Skills in Scratch

arXiv: https://arxiv.org/abs/2604.00730 · HTML: https://mlsanity.com/paper/2604.00730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [LitterBox: A Linter for Scratch Programs](https://mlsanity.com/paper/2102.07440/) (2021)
- [pycefr: Python Competency Level through Code Analysis](https://mlsanity.com/paper/2203.15990/) (2022)

## Abstract (arXiv)

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.