Advancing privacy in learning analytics using differential privacy

TL;DR

This paper introduces a novel differential privacy framework tailored for learning analytics, enhancing data privacy protection while maintaining utility, validated through experiments on real datasets.

Contribution

It presents the first DP framework specifically designed for learning analytics, with practical implementation guidance and validation.

Findings

DP effectively safeguards data privacy against attacks

Trade-offs between privacy and data utility are characterized

Framework supports practical adoption in LA research and practice

Abstract

This paper addresses the challenge of balancing learner data privacy with the use of data in learning analytics (LA) by proposing a novel framework by applying Differential Privacy (DP). The need for more robust privacy protection keeps increasing, driven by evolving legal regulations and heightened privacy concerns, as well as traditional anonymization methods being insufficient for the complexities of educational data. To address this, we introduce the first DP framework specifically designed for LA and provide practical guidance for its implementation. We demonstrate the use of this framework through a LA usage scenario and validate DP in safeguarding data privacy against potential attacks through an experiment on a well-known LA dataset. Additionally, we explore the trade-offs between data privacy and utility across various DP settings. Our work contributes to the field of LA by offering a practical DP framework that can support researchers and practitioners in adopting DP in their works.