<![CDATA[Background: Student-retention early-warning systems have substantially improved predictive performance, yet their outputs often remain limited to risk scores that provide little guidance for educational intervention.Aims: This study proposes and empirically evaluates a reproducible two-stage framework that integrates classroom interaction modeling, institutional retention prediction, and structured LLM-ready intervention rationales for student-retention decision support.Methods: The framework was evaluated using two complementary benchmark datasets. xAPI-Edu-Data was used for classroom interaction modeling, whereas Predict Students' Dropout and Academic Success was used for institutional retention prediction. The datasets were analyzed independently rather than merged. Stratified train-test splits, ensemble machine learning models, feature ablation, feature-importance analysis, probability calibration, and rationale-quality evaluation were employed.Result: LightGBM achieved the highest Macro-F1 (0.7775) on the xAPI-Edu-Data benchmark, while XGBoost produced the best overall performance on the institutional retention dataset, achieving a multiclass accuracy of 0.7672, a Macro-F1 of 0.6964, and a ROC-AUC of 0.8889. In binary dropout prediction, XGBoost achieved a ROC-AUC of 0.9360 and an Average Precision of 0.9101. Behavioral engagement, attendance, academic progression, and tuition-related variables consistently emerged as the most informative predictors. The structured rationale layer achieved complete evidence alignment, actionability, and monitoring specificity while generating 392 unique intervention rationales.Conclusion: The proposed framework demonstrates that early-warning analytics can move beyond risk prediction by integrating predictive analytics, explainable AI, and structured intervention rationale generation into a transparent, evidence-grounded decision-support workflow for improving student-retention decisions in higher education.]]>
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