Desperately seeking the impact of learning analytics in education at scale: Marrying data analysis with teaching and learning

TL;DR

This paper critically examines the current state of learning analytics in education, highlighting the gap between data availability and empirical evidence of its practical impact, and discusses barriers to effective implementation.

Contribution

It provides a comprehensive analysis of the challenges and barriers in applying learning analytics in educational practice, emphasizing the need to understand educators' needs and contexts.

Findings

Limited empirical evidence of LA's impact in practice

Barriers include data collection, interpretation, and educator support

Need for understanding educator needs and contextual factors

Abstract

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.