Be Careful What You Wish For! Learning Analytics and the Emergence of Data-Driven Practices in Higher Education

With the growing digitalization of the education sector, the availability of significant amounts of data, “big data,” creates possibilities for the use of artificial intelligence technologies to gain valuable insight into how students learn in higher education. Learning analytics technologies are examples of how deep learning algorithms can identify patterns in data and incorporate this “knowledge” into a model that is eventually integrated into the digital platforms used for interacting with students. This chapter introduces learning analytics as an emerging sociotechnical phenomenon in higher education. We situate the promises and expectations associated with learning analytics technologies, map their ties to emerging data-driven practices, and unpack the ethical concerns that are related to such practices via examples.Following this, we discuss three insights that we hope will provoke discussions among educators, researchers, and practitioners in higher education: (1) educational data-driven practices are highly context sensitive, (2) educational data-driven practices are not synonymous with evidence-based practices, and (3) innovative educational data-driven practices are not sustainable per se. This chapter calls for debating the role of emerging data-driven practices in higher education in relation to academic freedom and educational values embedded in critical pedagogy.