Scaling Up Learning Analytics in an Evidence-Informed Way

Abstract

Worldwide, the interest in learning analytics is rising, and higher educational institutions seek ways to benefit from the digital traces left behind by learners. The successful adoption of learning analytics comprises different phases, ranging from initialization to scaling. However, institutions with no or limited experience with learning analytics face many challenges when going through these phases. This chapter explores how institutions can proceed to implement learning analytics on a large scale. For this purpose, institutions need the right organizational capabilities as well as measures to assess the effect on learning. Based on literature and empirical data, we distinguish five critical learning analytics categories of organizational learning analytics capabilities: Data, Management, People, Technology, and Privacy & Ethics. The ability to develop these categories benefits the impact of learning analytics on learning. Furthermore, this chapter also provides operational definitions of affected learning. This enables institutions to assess the impact of learning analytics continuously. Based on learning 178theories, we identify three categories of learning that learning analytics can affect: learning process, learning outcome, and learning environment. The operational definitions we found during our research are classified accordingly. This allows educational institutions to measure, compare, and improve the effects of learning analytics on learning.