'Big Data' and 'Learning Analytics' have become prominent terms in education technology in recent times. In this block of the course, we will explore what these terms mean, how they have come to prominence, and what the implications might be for education in the 21st century.

Big Data is a term often used to describe large sets of data acquired from the web. It is, however, a contested term, and many have argued that large scale pre-digital studies, such as census research, might also constitute 'big data'. Nevertheless, it is a term that has risen to prominence following widespread use of the Internet, and public social media services in particular. The 'big' in 'big data' is often therefore used to mean the large numbers of participants involved. However, critical responses to big data have highlighted the minimal amounts of data associated with each person in such studies, and called instead for 'deep data' approaches that seek to capture more data about individuals. the key claims of big data are that more data gives researches more information with which to understand social phenomena, and for education this means supposedly a better understanding of how people learn. This raised crucial questions for those working in education: about whether the volume of information is more important that the quality of informal, about who decides on the processes of analysis and interpretation of data, and also about the ethics of widespread data collection.

'Learning Analytics' is the term used to describe the analysis of educational data specifically. It can therefore be understood as the specific educational response to 'big data', although learning analytics can also be applied to small data sets. It is however a term that has also risen to prominence following widespread use of the web, and online educational services specifically. While big data can refer to any computational processing of data, learning analytics refers to that done specifically in the context of education. Proponents of learning analytics claim that both the availability of increasing amounts of student data, as well as progress made in computational analysis, mean that new and important insights about the learning process can be understood through this emerging field of research. Critical responses have highlighted the behaviourist inclinations of learning analytics; a way of inferring learning from student behaviour that many in education now find problematic.

Key to this block is understanding the emerging ideas of big data and learning analytics through the critical and cultural perspectives we have developed in block 1. As we have seen, educational technologies tend to emerge with particular ideologies and assumptions attached to them - technological determinism for example, but also the idea that the very purpose of technology is to 'solve' the 'problem' of education and create a better future. So this week we are exploring how big data and learning analytics can be understood by 'looking to the past' and 'looking to the future'.

As in the previous blocks, the resources here are in the public domain, and are linked to more expansive information and activities around each of the topics. Therefore, while the resources below are mandatory for the course, you are encouraged to explore these topics further to broaden your understanding.