ABSTRACT
Over the past five years a number of factors, not least the changing economic climate and its impact on the educational sector, have led to increased awareness of the need for greater research, analysis and understanding of all aspects of data relating to education. Researchers in the emerging fields of educational data mining (EDM) and learning analytics have been active in exploring the potential of data created through the processes of teaching and learning. In parallel with these developments, interest has also increased in the potential of ‘big data’ and the application of business intelligence approaches to the educational sector. For example, can the recommendation systems commonly used by companies such as Amazon and Netflix be employed in educational contexts? The emergence of MOOCs (massive open online courses) can be seen as a key area where big data approaches can be applied to learning data sets, due to the massive scale of participation. In wider society the ‘quantified self movement’(Quantified Self, 2012), which involves using wearable sensors to self-monitor and gather data on different aspects of an individual’s life, physical state and performance, is giving rise to increased access to and reuse of personal data from multiple sources, including social network sites, geolocation services and data collected from mobile devices.
