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 ext...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11016664/ |
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| author | Daniel M. Muepu Yutaka Watanobe Md. Faizul Ibne Amin |
| author_facet | Daniel M. Muepu Yutaka Watanobe Md. Faizul Ibne Amin |
| author_sort | Daniel M. Muepu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-53c5ffcfb0744845a90883607aaa70c0 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-53c5ffcfb0744845a90883607aaa70c02025-08-20T03:07:28ZengIEEEIEEE Access2169-35362025-01-0113937129373410.1109/ACCESS.2025.357424611016664A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code AnalysisDaniel M. Muepu0https://orcid.org/0009-0000-0867-3697Yutaka Watanobe1https://orcid.org/0000-0002-0030-3859Md. Faizul Ibne Amin2https://orcid.org/0009-0001-0722-3536The University of Aizu, Aizu-Wakamatsu, Fukushima, JapanThe University of Aizu, Aizu-Wakamatsu, Fukushima, JapanThe University of Aizu, Aizu-Wakamatsu, Fukushima, JapanRecommending 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.https://ieeexplore.ieee.org/document/11016664/Recommender systemscontent-based filteringsource code analysisprogramming educationonline judge system |
| spellingShingle | Daniel M. Muepu Yutaka Watanobe Md. Faizul Ibne Amin A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code Analysis IEEE Access Recommender systems content-based filtering source code analysis programming education online judge system |
| title | A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code Analysis |
| title_full | A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code Analysis |
| title_fullStr | A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code Analysis |
| title_full_unstemmed | A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code Analysis |
| title_short | A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code Analysis |
| title_sort | comprehensive content based recommendation system for programming problems through multi faceted code analysis |
| topic | Recommender systems content-based filtering source code analysis programming education online judge system |
| url | https://ieeexplore.ieee.org/document/11016664/ |
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