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

Title: 面向公平和隐私感知的教育预测建模迁移学习：社区学院保留预测案例研究

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. Show Chinese abstract

Abstract: 预测分析在学习分析中被广泛使用，但许多资源有限的机构缺乏开发自己模型的能力，或者依赖于在不同情境下训练的专有模型，这些模型透明度较低。 迁移学习有望扩大预测分析的公平获取，但由于法律和技术限制，仍研究不足。 本文探讨了美国两年制社区学院的保留预测迁移学习策略。 我们设想一种场景，即社区学院相互合作，并与四年制大学合作，在隐私约束下开发保留预测模型，并评估跨机构模型迁移的风险和改进策略。 利用来自4所研究型大学和23所社区学院的行政记录，覆盖7个群体超过80万学生，我们发现当外部模型在本地部署而没有适应时，性能和公平性会下降。 公开可用的上下文信息可以预测这些性能下降，并为模型可移植性提供早期指导。 对于受隐私法规限制的开发者，基于人口统计相似性的机构顺序训练可以提高公平性而不影响性能。 对于缺乏本地数据来微调源模型的机构，针对敏感群体定制评估阈值在提高性能和公平性方面优于标准迁移技术。 我们的研究结果表明迁移学习在更易获得的教育预测建模中的价值，并呼吁在模型训练、选择和部署中谨慎使用上下文信息，以实现可靠和公平的模型迁移。