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Personalized Learning Analytics Through Static Code Analysis in Computer Science Education
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AI
DOI：10.18267/j.aip.283.png)
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Abstract
En 中文
Background: Learning programming is often difficult for beginners, primarily because of the challenge of providing timely and personalized feedback in large educational environments. While automated assessment systems have improved efficiency in grading and feedback, they typically focus on correctness and often lack personalized guidance concerning code quality, readability, and maintainability. Objective: This study aims to investigate whether integrating static code analysis into automated assessment systems to provide personalized feedback can effectively enhance students code quality, learning process, and engagement in programming courses. Methods: We designed a personalized feedback system integrated with static analysis tools (Cppcheck and Clang-format), deployed within an existing automated assessment platform used by undergraduate programming students. The system was evaluated in a controlled experiment involving 60 students randomly divided into control and treatment groups. The effectiveness of personalized feedback was measured through quantitative metrics (style violations, potential bugs, and design issues), qualitative surveys, and submission behaviours over multiple assignments. Results: Results demonstrated that students receiving personalized feedback improved their code quality, reducing the number of style violations by 76%, potential bugs by 52%, and structural issues by 32% compared to the control group. Students also expressed higher satisfaction, increased motivation, and greater willingness to iteratively refine their code based on personalized feedback. Conclusion: The integration of static code analysis for personalized feedback not only enhances code quality but also helps a deeper understanding of good programming practices among students. Future research should focus on making feedback systems more adaptive, incorporating intelligent tutoring techniques, and exploring long-term impacts on programming habits and skill retention.
Keywords:
Personalized feedback
Static code analysis
Student engagement
Code quality improvement
Learning analytics
Student-centered feedback
Clean code principles
Code quality metrics
Interactive feedback
Journal
A
IF:
1.1
Papers:
15
Citations:
0