🤖 AI Summary
Existing student performance prediction models are predominantly static and lack adaptability to post-intervention data, limiting their utility in dynamic educational settings. Method: We propose a feedback-driven, closed-loop self-optimizing decision support system that incrementally re-trains a LightGBM regression model using newly acquired post-intervention grades. The system features an interactive Flask-based web interface, integrates SHAP for feature attribution and model interpretability, and supports interoperability with Learning Management Systems (LMS) and institutional dashboards. Contribution/Results: By transforming static prediction into an online-evolving intelligent tutoring aid, the system enhances transparency and responsiveness in human-AI collaborative decision-making. Experimental evaluation demonstrates a 10.7% reduction in RMSE after incremental re-training and a consistent upward trend in predicted scores for intervened students, validating its effectiveness and adaptability in real-world educational environments.
📝 Abstract
Accurate prediction of student performance is essential for timely academic intervention. However, most machine learning models in education are static and cannot adapt when new data, such as post-intervention outcomes, become available. To address this limitation, we propose a Feedback-Driven Decision Support System (DSS) with a closed-loop architecture that enables continuous model refinement. The system integrates a LightGBM-based regressor with incremental retraining, allowing educators to input updated student results, which automatically trigger model updates. This adaptive mechanism improves prediction accuracy by learning from real-world academic progress. The platform features a Flask-based web interface for real-time interaction and incorporates SHAP for explainability, ensuring transparency. Experimental results show a 10.7% reduction in RMSE after retraining, with consistent upward adjustments in predicted scores for intervened students. By transforming static predictors into self-improving systems, our approach advances educational analytics toward human-centered, data-driven, and responsive AI. The framework is designed for integration into LMS and institutional dashboards.