As if often the case in Wikipedia, the article on Learning Analytics began as a quick summary and rapidly mushroomed into a far more extensive treatise on the subject. However - the initial definition has had few versions. It changed in the first day, then again a couple of years later, but the sentence written in 2013 is the same as the sentence which opens the article today. The difference is that in today's article this opening sentence is followed by over 4000 words of further information.
Learning analytics is the use of data and models to predict student progress and performance, and the ability to act on that information - 23rd August 2010
Learning analytics is the use of intelligent data,
learner-produced data, and analysis models to discover information and
social connections, and to predict and advise on learning. - 24th August 2010
Learning analytics is the measurement, collection, analysis and
reporting of data about learners and their contexts, for purposes of
understanding and optimising learning and the environments in which it
occurs. - 1st September 2013
I usually like to begin my investigations about an unfamiliar subject with a read of the associated Wikipedia article. I realize that it's not a peer reviewed, 'reliable' source but it is often succinct, accessible (especially to to the non-expert) and well written with good clarity. The learning analytics article is none of these things and it reads as an article written by committee (which is, of course, exactly what it is!).
The impression that the whole article gives me is that the subject of 'Learning Analytics' is as vast, as nebulous, as complex and as multifaceted as the two words would imply. H800 challenged every internal impression and idea I had about the concept of 'learning' so I am keenly aware of how 'simple' ideas can become mosaic when investigated and the word 'analytics' gives us no expectation of a simple and easily defined concept! Put two big concepts together and the creation of a gargantuan concept seems also inevitable!
The simple sentences above describe aspects of learning analytics. My impression is not that those who change the definition claim what is stated is incorrect, but that it's incomplete and inadequate. The extra information, text, ideas and paragraphs don't detract from what has been previously written as much as adding to, augmenting and complementing it. There are a multitude of associated concepts which overlap with Learning Analytics but the edges of each concept is blurry and undefined.
I suspect a concise definition which will satisfy everyone is impossible to develop but by looking at the areas everyone agrees with we can draw some conclusions. Such commonalities include:
Data - the data is described as 'intelligent' and processes related to collecting, collating and analysing this data are all part of the definitions. Data is an inescapable part of Learning Analytics. It's the key ingredient and without data there can be no analytics.
Discover, understand - data can enable a great deal of knowledge to be amassed and that knowledge can lead to understanding of crucial patterns within learning and teaching
Prediction, modelling, advising and optimising - four different but overlapping ideas in this context. The way in which the data is used is part of what makes Learning Analytics what it is. The purpose of LA is, at least in part, the improvement of the learning journey for the individual and the cohort.
Learning Analytics according to Wikipedia
As if often the case in Wikipedia, the article on Learning Analytics began as a quick summary and rapidly mushroomed into a far more extensive treatise on the subject. However - the initial definition has had few versions. It changed in the first day, then again a couple of years later, but the sentence written in 2013 is the same as the sentence which opens the article today. The difference is that in today's article this opening sentence is followed by over 4000 words of further information.
Learning analytics is the use of data and models to predict student progress and performance, and the ability to act on that information - 23rd August 2010
Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning. - 24th August 2010
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. - 1st September 2013
I usually like to begin my investigations about an unfamiliar subject with a read of the associated Wikipedia article. I realize that it's not a peer reviewed, 'reliable' source but it is often succinct, accessible (especially to to the non-expert) and well written with good clarity. The learning analytics article is none of these things and it reads as an article written by committee (which is, of course, exactly what it is!).
The impression that the whole article gives me is that the subject of 'Learning Analytics' is as vast, as nebulous, as complex and as multifaceted as the two words would imply. H800 challenged every internal impression and idea I had about the concept of 'learning' so I am keenly aware of how 'simple' ideas can become mosaic when investigated and the word 'analytics' gives us no expectation of a simple and easily defined concept! Put two big concepts together and the creation of a gargantuan concept seems also inevitable!
The simple sentences above describe aspects of learning analytics. My impression is not that those who change the definition claim what is stated is incorrect, but that it's incomplete and inadequate. The extra information, text, ideas and paragraphs don't detract from what has been previously written as much as adding to, augmenting and complementing it. There are a multitude of associated concepts which overlap with Learning Analytics but the edges of each concept is blurry and undefined.
I suspect a concise definition which will satisfy everyone is impossible to develop but by looking at the areas everyone agrees with we can draw some conclusions. Such commonalities include: