Deep Learning-Driven Student Performance Analysis: Detecting Anomalies and Predicting Academic Success

Abstract

Accurately predicting student performance and identifying anomalies in academic datasets has become increasingly crucial for enhancing educational outcomes and enabling data-driven interventions in modern learning environments. Traditional statistical methods and conventional machine learning approaches often struggle with the multidimensional nature and increasing scale of contemporary student datasets, which encompass diverse academic, behavioral, and socio-demographic variables. This study explores advanced deep learning techniques; including Autoencoders for unsupervised anomaly detection, Recurrent Neural Networks with Long Short-Term Memory architectures for temporal pattern recognition, and Deep Neural Networks for comprehensive performance prediction to address these challenges. The proposed framework demonstrates significant improvements in detecting subtle performance anomalies that often precede academic difficulties, while simultaneously predicting longitudinal success patterns with greater accuracy than traditional methods. By leveraging the hierarchical feature learning capabilities of deep architectures, our system enables early identification of at risk students through continuous analysis of complex, nonlinear relationships in educational data, allowing institutions to implement timely, personalized interventions. Research studies have empirically validated the effectiveness of these models in educational contexts, showing superior performance in measuring student achievement patterns and predicting learning outcomes. The findings contribute to theoretical advancements in educational analytics but also provide practical insights for curriculum designers and policy makers seeking to optimize instructional strategies. Furthermore, the study establishes significant benchmarks for educational contexts by demonstrating how deep learning can enhance both teaching methodologies and student support systems through data-driven insights. This research makes a substantial contribution to the growing field of Educational Data Mining by proposing a robust deep learning framework that serves as both a predictive tool and a baseline for future studies in student performance analysis, while also addressing critical challenges in model interpretability and implementation scalability within real-world educational settings.

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