A CEFR-Inspired Classification Framework with Fuzzy C-Means To Automate Assessment of Programming Skills in Scratch
arXiv:2604.00730v2 Announce Type: replace-cross Abstract: 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 project
The increasing demand for scalable and objective programming skill assessment, especially in educational and training contexts, necessitates automated and standardized methods.
This development offers a standardized, data-driven approach to evaluating programming proficiency, which can significantly improve STEM education, workforce training, and curriculum development.
Programming skill assessment can become more objective, personalized, and efficient, moving beyond manual and subjective evaluations to a more scalable and data-informed system.
- · Educational institutions
- · EdTech platforms
- · Students
- · Curriculum designers
- · Subjective manual assessment processes
Automated, standardized assessment of programming skills becomes more widespread, enabling personalized learning pathways.
Improved data on programming proficiency leads to more targeted and effective educational interventions and skill development programs.
A standardized global benchmark for programming skills could emerge, influencing international education and workforce mobility policies.
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