Analysis of Student Performance Prediction Using the Bi‐LSTM Technique Focused on the Indian Higher Education Context Using a Synthetic Dataset

ABSTRACT Student performance prediction is a critical task for improving educational outcomes and enabling data‐driven decision‐making in higher education. This study proposes a context‐specific predictive framework for Indian higher education that integrates AISHE institutional indicators with student‐level behavioral data through multi‐level data fusion, incorporates SHAP‐based interpretability to support policy and intervention decisions, and employs a Bi‐LSTM + Nadam architecture to achieve strong predictive performance. The framework integrates multi‐level heterogeneous data, combining institutional indicators from AISHE with student‐level academic, behavioral, and demographic features. To address the lack of unified large‐scale datasets, a synthetic data generation approach is employed to simulate multi‐institutional environments with temporal student records, supported by a mapping mechanism aligning student and institutional attributes. Experimental results demonstrate that the proposed model achieves 93.1% accuracy, significantly outperforming traditional machine learning approaches by approximately 13%. The model maintains precision of 93.0%, recall of 93.1%, and F1‐scores of 93% across all performance categories, confirming robust and balanced classification. Convergence analysis shows that Nadam enables faster and more stable training, while the Bi‐LSTM architecture improves performance by 1.9% over unidirectional LSTM by capturing bidirectional temporal dependencies. Feature importance analysis reveals that academic and behavioral factors dominate prediction, with GPA, attendance, and assignment submission contributing approximately 42.7% of the total normalized SHAP importance, while demographic factors have minimal influence, ensuring reduced bias. ROC‐AUC analysis confirms excellent discrimination across all categories, with a macro‐average AUC of 0.993 (range: 0.989–0.997), fully consistent with the reported accuracy and recall metrics. The proposed framework is suitable for early warning systems, targeted interventions, and academic decision support, making it highly applicable in real‐world educational settings.