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Learning personalization in block-based programming languages using clustering and static code analysis
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DOI：10.1016/j.caeo.2026.100333.png)
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Abstract
En 中文
• A data-driven method to personalize programming education is presented. • Analysis of large datasets from novice Visual Programming Language (VPL) projects. • Visual and personalized recommendations are integrated within MIT App Inventor. • Static code analysis and Machine Learning identify patterns and skill levels. • Personalized learning paths are generated based on clustering results.
Keywords:
Learning personalization
Block-based languages
Machine learning
K-Means
Hierarchical Agglomerative
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