This paper from Rebecca Ferguson gives a concise and ordered review of a burgeoning and almost chaotic subject development! It's ironic that something which sounds as definitive as 'learning analytics' can contain so much nuance and so many different opinions. It seems that the term came into use in several contexts simultaneously and was used differently each time.
I feel that the three bullet points on page 9 crystallise the current issues best:
Educational data mining focused on the technical challenge: How can we extract value from these big sets of learning-related data?
Learning analytics focused on the educational challenge: How can we optimise opportunities for online learning?
Academic analytics focused on the political/economic challenge: How can we substantially improve learning opportunities and educational results at national or international levels?
In short - we are now generating huge amounts of data - shouldn't we use it? Maybe we could help individuals learn better and more by using the data to create and refine excellent opportunities and maybe this data could be applied at a national (and international) level to improve learning for entire populations.
Ferguson 2012
This paper from Rebecca Ferguson gives a concise and ordered review of a burgeoning and almost chaotic subject development! It's ironic that something which sounds as definitive as 'learning analytics' can contain so much nuance and so many different opinions. It seems that the term came into use in several contexts simultaneously and was used differently each time.
I feel that the three bullet points on page 9 crystallise the current issues best: