Modeling Programming Skills with Source Code Embeddings for Context-aware Exercise Recommendation
This work addresses the problem of personalized exercise recommendation for students in introductory programming courses, representing an incremental improvement over existing methods.
The paper tackled the problem of recommending programming exercises by modeling students' skills using source code embeddings, which outperformed token-based and graph-based alternatives in predicting skills and provided more suitable recommendations than baselines based on correctness or solution time.
In this paper, we propose a context-aware recommender system that models students' programming skills using embeddings of the source code they submit throughout a course. These embeddings predict students' skills across multiple programming topics, producing profiles that are matched to the skills required by unseen homework problems. To generate recommendations, we compute the cosine similarity between student profiles and problem skill vectors, ranking exercises according to their alignment with each student's current abilities. We evaluated our approach using real data from students and exercises in an introductory programming course at our university. First, we assessed the effectiveness of our source code embeddings for predicting skills, comparing them with token-based and graph-based alternatives. Results showed that Jina embeddings outperformed TF-IDF, CodeBERT-cpp, and GraphCodeBERT across most skills. Additionally, we evaluated the system's ability to recommend exercises aligned with weekly course content by analyzing student submissions collected over seven course offerings. Our approach consistently produced more suitable recommendations than baselines based on correctness or solution time, indicating that predicted programming skills provide a stronger signal for problem recommendation.