Integrating categorical and continuous data in a cluster-then-classify methodology for predicting undergraduate student success

Student retention in higher education remains a significant challenge despite decades of research. This study introduces a novel cluster-label-classify methodology to predict at-risk students and identify common characteristics among those who drop out. Using data from a small private Midwestern university in the United States, we first applied the K-Prototypes algorithm to cluster non-retained students into five groups based on both numeric and categorical variables. We then labeled these clusters and the retained students, creating a multi-class classification problem. Finally, we used a Gradient Boosting Classifier and XGBoost for classification, achieving F1 scores of 0.82 to 0.89 for predicting non-retained students and 0.96 for retained students after addressing class imbalance with SMOTE. This approach allows for customized labeling specific to each institution and enables more targeted interventions for at-risk students. Our methodology combines demographic, academic, and socioeconomic factors to provide a comprehensive view of student retention, potentially offering new insights into this longstanding issue in higher education. The paper also discusses algorithmic bias, examining potential fairness issues in the predictive models and their implications for different student populations. Finally, a discussion on Privacy Preserving Machine Learning (PPML) provides future strategies for testing how these technologies generalize to other institutions while enhancing the privacy of student data.