Collection and Analysis of Source Code Metrics for Composition of Programming Learning Profiles

Abstract

This paper presents an approach for the application of clustering algorithms to uncover computer programming learning profiles by using evidence extracted from source code metrics. A system for automatic assessment of programming activities featuring capture of source code metrics was developed, in order to build a dataset containing metrics extracted from programs developed by beginners in a computing course. The dataset was submitted to three clustering algorithms. The results were promising when clustering students according to these indicators using the K-means algorithm.