  Learning analytics (LA) is argued to be able to improve learning outcomes,
learner support and teaching. However, despite an increasingly expanding amount
of student (digital) data accessible from various online education and learning
platforms and the growing interest in LA worldwide as well as considerable
research efforts already made, there is still little empirical evidence of
impact on practice that shows the effectiveness of LA in education settings.
Based on a selection of theoretical and empirical research, this chapter
provides a critical discussion about the possibilities of collecting and using
student data as well as barriers and challenges to overcome in providing
data-informed support to educators' everyday teaching practices. We argue that
in order to increase the impact of data-driven decision-making aimed at
students' improved learning in education at scale, we need to better understand
educators' needs, their teaching practices and the context in which these
practices occur, and how to support them in developing relevant knowledge,
strategies and skills to facilitate the data-informed process of digitalization
of education.
