🤖 AI Summary
This work addresses the lack of fine-grained, quantitative assessment of learning processes and code quality in current programming education, which hinders accurate diagnosis of students’ comprehension and instructional efficacy. The authors propose a plugin system integrated into mainstream code editors that, for the first time, adapts industrial-grade development log analysis to educational contexts. By continuously capturing students’ coding behaviors, error messages, and progress data in real time, the system constructs a timestamp-driven behavioral tracking model to derive quantitative metrics. This approach enables structured recording and analysis of programming activities, facilitating timely evaluation of instructional content, identification of common learning bottlenecks, and the creation of an open programming behavior database tailored for educational research and personalized learning.
📝 Abstract
Software developers use metrics to evaluate code quality and productivity, but these practices are still rare in programming education. This project bridges the gap by collecting real-time learning analytics from individual student and whole-class code development logs. This granular, quantitative data provides educators with qualitative insights into the learning process. It allows them to evaluate student comprehension, identify common challenges, and critically assess whether the allocated time for exercises and algorithms is sufficient for mastery. Unlike traditional Learning Management Systems, we propose a novel approach: a plugin for a widely used code editor that captures granular interactions during programming and documentation. The resulting dataset logs coding behaviors, errors, and progress, enabling evidence-based analysis of learning patterns and educational benchmarking. By structuring this real-time programming trail, we support research on teaching methodologies, learner challenges, and skill acquisition. Quantitative metrics complement qualitative assessment by evaluating code, exercise progress, and timestamp logs. Our goal is to provide an open-access database for educators and researchers, fostering data-driven insights to enhance instruction and personalize learning experiences. This work aligns industrial best practices with pedagogical innovation, advancing measurable, empirical approaches to programming education.