An Approach to Improve k-Anonymization Practices in Educational Data Mining (Journal of Educational Data Mining, 2024)
Educational data mining has allowed for large improvements in educational outcomes and understandingof educational processes. However, there remains a constant tension between educational data mining advancesand protecting student privacy while using educational datasets. Publicly available datasets havefacilitated numerous research projects while striving to preserve student privacy via strict anonymizationprotocols (e.g., k-anonymity); however, little is known about the relationship between anonymizationand utility of educational datasets for downstream educational data mining tasks, nor how anonymizationprocesses might be improved for such tasks. We provide a framework for strictly anonymizing educationaldatasets with a focus on improving downstream performance in common tasks such as studentoutcome prediction. We evaluate our anonymization framework on five diverse educational datasets withmachine learning-based downstream task examples to demonstrate both the effect of anonymization andour means to improve it. Our method improves downstream machine learning accuracy versus baselinedata anonymization by 30.59%, on average, by guiding the anonymization process toward strategies thatanonymize the least important information while leaving the most valuable information intact.