Learning analytics and data ethics in performance data management: a benchlearning exercise involving six European universities

Abstract

ABSTRACT Drawn from the SQELT Erasmus+ project, this article explores how learning analytics is implemented at a set of six European universities in the context of their performance data management models, including its multiple functions and ethical issues. It further identifies possible good practice and policy recommendations at decision-making level. Results show that learning analytics is present to a certain extent in all six institutions, although mostly based on traditional data and still lacking predictive capacity concerning students’ performance. Learning analytics is viewed as useful in providing more accurate personal data on students’ learning, contributing to the establishment of more sophisticated quality management systems. The European General Data Protection Regulation and national privacy laws sufficiently cover the majority of data ethics risks posed by learning analytics. Overall, learning analytics entails both opportunities and threats. The possibilities of a learning analytics approach deserve further attention within universities and quality assurance agencies.