Harnessing Large-Scale University Registrar Data for Predictive Insights: A Data-Driven Approach to Forecasting Undergraduate Student Success with Convolutional Autoencoders

Predicting undergraduate student success is critical for informing timely interventions and improving outcomes in higher education. This study leverages over a decade of historical data from Louisiana State University (LSU) to forecast graduation outcomes using advanced machine learning techniques, with a focus on convolutional autoencoders (CAEs). We detail the data processing and transformation steps, including feature selection and imputation, to construct a robust dataset. The CAE effectively extracts meaningful latent features, validated through low-dimensional t-SNE visualizations that reveal clear clusters based on class labels, differentiating students likely to graduate from those at risk. A two-year gap strategy is introduced to ensure rigorous evaluation and simulate real-world conditions by predicting outcomes on unseen future data. Our results demonstrate the promise of CAE-derived embeddings for dimensionality reduction and computational efficiency, with competitive performance in downstream classification tasks. While models trained on embeddings showed slightly reduced performance compared to raw input data, with accuracies of 83% and 85%, respectively, their compactness and computational efficiency highlight their potential for large-scale analyses. The study emphasizes the importance of rigorous preprocessing, feature engineering, and evaluation protocols. By combining these approaches, we provide actionable insights and adaptive modeling strategies to support robust and generalizable predictive systems, enabling educators and administrators to enhance student success initiatives in dynamic educational environments.