Towards Fair and Privacy-Aware Transfer Learning for Educational Predictive Modeling: A Case Study on Retention Prediction in Community Colleges

TL;DR

This study explores transfer learning for retention prediction in community colleges, demonstrating how privacy constraints and contextual information influence model fairness and performance across institutions.

Contribution

It introduces strategies for privacy-aware transfer learning in educational settings, highlighting methods to improve fairness and accuracy without compromising privacy.

Findings

Transfer learning can improve retention prediction fairness and performance.

Contextual information helps forecast and mitigate model performance drops.

Sequential training based on demographic similarities enhances fairness.

Abstract

Predictive analytics is widely used in learning analytics, but many resource-constrained institutions lack the capacity to develop their own models or rely on proprietary ones trained in different contexts with little transparency. Transfer learning holds promise for expanding equitable access to predictive analytics but remains underexplored due to legal and technical constraints. This paper examines transfer learning strategies for retention prediction at U.S. two-year community colleges. We envision a scenario where community colleges collaborate with each other and four-year universities to develop retention prediction models under privacy constraints and evaluate risks and improvement strategies of cross-institutional model transfer. Using administrative records from 4 research universities and 23 community colleges covering over 800,000 students across 7 cohorts, we identify performance and fairness degradation when external models are deployed locally without adaptation. Publicly available contextual information can forecast these performance drops and offer early guidance for model portability. For developers under privacy regulations, sequential training selecting institutions based on demographic similarities enhances fairness without compromising performance. For institutions lacking local data to fine-tune source models, customizing evaluation thresholds for sensitive groups outperforms standard transfer techniques in improving performance and fairness. Our findings suggest the value of transfer learning for more accessible educational predictive modeling and call for judicious use of contextual information in model training, selection, and deployment to achieve reliable and equitable model transfer.