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This paper attempts to consider 'learning analytics' from a variety of academic perspectives rather than concentrating solely on education.

The aim of the authors was to identify trends and also assess the most influential voices within the field of learning analytics. As well as individual voices the authors also noted that multiple disciplines were writing about learning analytics and that the relative contribution to the overall conversation between different disciplines was not equal in quantity or influence. Their method was to analyse citations and map their use in a structured network. The assumption was the papers most regularly cited, and by the widest range of contributors, could be considered as being more significant and more likely to be moving the discipline forward.

The observation was that the discipline of education – with its easy access to vast quantities of data – was not being as innovative in using that data as one might expect. Education was using simple demographic data alongside easy checkpoints such as student retention and outcomes. The suggestion was made that the data being collected could be used to contribute to better learning and teaching but, at the time of writing, it was not being used that way.

Education may seem the obvious discipline which will both discuss and utilise learning analytics the paper makes clear that other disciplines are also taking the discipline forward including psychology, philosophy, sociology, linguistics, learning sciences, statistics, machine    learning/artificial intelligence and computer science.

The authors found that the major disciplines – computer science and education – were diverging and that learning analytics was thus going in more than one direction.

They also found that the most commonly cited papers were not empirical research but more conceptual in nature.

The use of ‘low hanging fruit’ (readily available data) is also discussed with hope that better and more useful learning analytics will develop.

The use of citation networks enables the authors to see where concentrations of papers are being published and how they link to one another. They can assess where ‘crossover’ papers develop which feed into the discussion in more than one academic discipline.

It would be easy to assume that the most regularly cited papers are the most insightful, methodologically consistent and ground-breaking. This would be, I think, an over simplification. Certain journals are more widely read within certain disciplines and the specific place a paper is published will determine, to a great extent, its audience.

I can see the value in this kind of analysis. Where many different researchers from different academic backgrounds are all looking at the same subject – albeit from different angles and with different motives – the potential for a ‘big picture’ (and overarching theory) to emerge is an engaging prospect. I also can see how the varied angles and motives can enable each different discipline to consider new ideas and take their own understanding of, and use of, learning analytics forward.  


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