Evaluating Federated Learning for At-Risk Student Prediction: A Comparative Analysis of Model Complexity and Data Balancing
By: Rodrigo Tertulino
Potential Business Impact:
Finds students needing help before they fall behind.
High dropout and failure rates in distance education pose a significant challenge for academic institutions, making the proactive identification of at-risk students crucial for providing timely support. This study develops and evaluates a machine learning model based on early academic performance and digital engagement patterns from the large-scale OULAD dataset to predict student risk at a UK university. To address the practical challenges of data privacy and institutional silos that often hinder such initiatives, we implement the model using a Federated Learning (FL) framework. We compare model complexity (Logistic Regression vs. a Deep Neural Network) and data balancing. The final federated model demonstrates strong predictive capability, achieving an ROC AUC score of approximately 85% in identifying at-risk students. Our findings show that this federated approach provides a practical and scalable solution for institutions to build effective early-warning systems, enabling proactive student support while inherently respecting data privacy.
Similar Papers
Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features
Machine Learning (CS)
Helps schools predict students needing help privately.
Centralized vs. Federated Learning for Educational Data Mining: A Comparative Study on Student Performance Prediction with SAEB Microdata
Machine Learning (CS)
Helps schools teach students better, privately.
Privacy-Preserved Automated Scoring using Federated Learning for Educational Research
Machine Learning (CS)
Schools share test answers without sharing student data.