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Eng."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>This study presents a time-series-based machine learning approach to predict final grades in a C programming course, leveraging temporal and behavioral features to support early intervention. We evaluated classification and regression models using three-class (Needs Improvement, Average, Excellent) and five-class (Fail, Poor, Average, Good, Excellent) grading schemes, addressing class imbalance with RandomOverSampler, SMOTE and ADASYN. Advanced sampling strategies, particularly SMOTE, enhanced minority class prediction in the three-class scheme, with XGBoost achieving superior performance. The five-class scheme offered finer granularity, revealing nuanced patterns in mid-tier performance through practice-related features, but faced challenges from increased class imbalance. Regression models, while suitable for continuous prediction, underperformed due to thresholding biases. SHAP analysis identified historical average score and difficulty-adjusted score as key predictors, providing actionable insights for educators. These findings highlight the trade-offs between broad and fine-grained prediction, with the three-class scheme supporting robust interventions and the five-class scheme enabling nuanced feedback. Future work includes incorporating qualitative features and hybrid approaches to improve fine-grained prediction and generalizability across educational contexts.<\/jats:p>","DOI":"10.1142\/s0218194025500548","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T06:41:45Z","timestamp":1756968105000},"page":"1763-1787","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Grade Prediction in Programming Education Using Time-Series XGBoost and SHAP Analysis"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3264-1681","authenticated-orcid":false,"given":"Jing","family":"Qiu","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, P. R. 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