Open data, private learners: a de-identified student activity and performance dataset for learning analytics

Abstract

The data-driven study and optimization of learning processes has become possible through collecting the digital traces left by learners as they interact with digital study materials and learning tools — a field known as Learning Analytics (LA). The increased availability of such data has fueled the growth of the LA field, enabling the development of new frameworks and models, and supporting the transfer of findings across domains, and collaboration on joint educational initiatives. However, given the sensitive and personal nature of intermediary learning results and outcomes, such data availability also raises significant ethical and privacy concerns. To support and foster the open and transparent development and evaluation of LA solutions, we present a detailed clickstream dataset collected at KU Leuven across two first-year bachelor courses over three academic years. The public dataset is accompanied by transparent documentation of the de-identification process, and we report on privacy and utility validation results.