On the Practicality of Differential Privacy for Knowledge Tracing
Year: 2025
Type: conference-paper
Abstract: The application of machine learning techniques to educational datasets has great potential to improve our understanding of learning processes. However, there are privacy concerns in the community about the datasets and models that are being developed using student interactions. This limits the development of AI in education. Differential privacy provides a mathematical framework that limits what can be inferred about an individual in a data set. In this study, we apply differential privacy techniques to knowledge tracing models and analyze the privacy-utility trade-off across three distinct privacy accounting methods. Our assessment offers insights into the practical challenges of implementing privacy-preserving knowledge tracing. We define differential privacy at the user level, where protection extends to a student's entire sequence of interactions. Our study shows that knowledge tracing models respond differently to privacy constraints, with deep knowledge tracing models maintaining competitive performance under differential privacy. We address important considerations for application of differential privacy, including optimal hyperparameter selection and evaluation of privacy accounting methods that balance strong privacy guarantees with model performance.
Authors: Anika Kabir, Chandan Tankala, Daniel Lowd
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6968099342