This chapter describes three characteristics of Learning Analytics work that distinguish it from prior educational research to give readers a concise overview of what makes learning analytics a unique and especially promising technology to improve teaching and learning. Data used in learning analytics relate to the process of learning, can come from a variety of sources (in both virtual and physical learning environments), and are characterized by their large quantity and relatively small grain size. Analysis approaches aim at detecting underlying patterns and relationships in the data and include prediction, structure discovery, temporal, language-based and visual methods. Pedagogical uses are what position learning analytics as more than simply a new set of methods but an impactful technology to drive data-informed decision-making through tailoring educational experiences, informing student self-direction, and supporting instructor planning and orchestration. The chapter concludes with an overview of the systemic and societal issues surrounding learning analytics use that frame how and to what extent they are able to affect education.
|Original language||English (US)|
|Title of host publication||In O. Adesope & A.G. Rudd (Eds.) Contemporary technologies in education: maximizing student engagement, motivation, and learning|
|Editors||Olusola Adesope , A. G. Rudd|
|Place of Publication||New York|
|State||Published - 2019|
Wise, A. F. (2019). Learning analytics: Using data-informed decision-making to improve teaching and learning. In O. Adesope , & A. G. Rudd (Eds.), In O. Adesope & A.G. Rudd (Eds.) Contemporary technologies in education: maximizing student engagement, motivation, and learning (pp. pp. 119-143)). Palgrave Macmillan.