A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code Analysis

Recommending exercises in educational contexts requires balancing relevance and diversity to support effective learning progression. In such settings, content-based recommendation is particularly suitable, as it aligns with specific learning objectives and supports progression without relying on extensive user interaction history. This study introduces a content-based recommendation system (CBRS) designed to suggest programming exercises based on intrinsic characteristics of source code. The system evaluates syntactic, structural, statistical, complexity, and semantic attributes to provide a comprehensive assessment of exercise similarity. The approach addresses the challenge of identifying exercises that require similar knowledge or complexity levels to those previously completed by students. This capability is especially beneficial when students struggle with specific concepts, as it enables the recommendation of additional exercises targeting the same content to reinforce understanding and promote mastery. Each feature was first evaluated individually to determine its contribution to recommendation effectiveness. Selected combinations were then tested to examine how different attributes influence recommendation quality in terms of relevance and diversity. The full integration of all features resulted in a precision of 0.70, recall of 0.77, MRR of 0.64, coverage of 0.63, serendipity of 0.49, and novelty of 0.51. The proposed CBRS demonstrates strong alignment with learners’ exercise histories and the potential to generate recommendations that balance familiarity with exploration. Compared with collaborative filtering models on the same dataset, the CBRS achieved stronger performance in diversity-focused metrics, supporting more engaging and meaningful learning experiences.