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Anna Greathead

Ferguson 2012

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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.
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Anna Greathead

National Analysis of National Learning Trends

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Learning analytics is clearly a term which is used to describe a particular phenomena which can happen on a wide range of scales.

Personally I am interested in how my individual socio-demographic circumstances can predict my success (or otherwise) in any aspect of learning and how interventions can be targeted to assist me. I am not entirely selfish - I am also interested in how this can be applied to other people!

Professionally part of my role is to tutor aspiring medics and dentists for the UCAT exam (formerly UKCAT) which is an aptitude test which must be taken as part of an application to most medical / dental schools. Upon registration the UCAT consortium ask each applicant to complete a series of socio-demographic questions - racial group, gender, level of education reached by parents (the vast majority of applicants are 17 years old) and the job title of their parents (presumably used, alongside education, as a proxy for social class). They already have the applicants post code and know if the applicant lives in a household with low enough income to qualify for a bursary. This information is not supplied to universities but is reportedly used to improve the UCAT for future cohorts. Pilot questions are trialed with a view to ensuring they are as equitable as possible. I imagine that groups of statisticians gather around data sets and assess why various questions performed better with people who had parents with higher educational qualifications, or who shared a similar ethnic group. (Incidentally - I teacher friend of mine got very frustrated one year when the disadvantaged children who attended her inner city primary school were faced with SATS questions about going to the theatre, and using a honey dipper - two activities which were entirely unknown to most of the children she taught. A huge failing in learning analytics or a cynical attempt to maintain existing privilege? Who knows!)

The articles we read in this activity looked at early learning analytics on a national scale. I also find this very interesting. The variation between educational styles and ethos between different countries is driven by many factors including culture, politics, and resources and comparisons can be meaningful and meaningless depending on how well the complexities of the issues are understood.

The first paper compares the USA to other countries - noting not only a lack of progress when compared to nations of similar levels of development and wealth - but also an up and coming threat from developing nations with very large populations.

The second paper introduces the idea of using analytics to shape future US educational policy at the national level with a coherent and unified plan. Affordability and value are key values but a need to match education to likely economic requirements is also significant.

The basic premise of these discussions assumes that there is a direct correlation between graduate level education and national prosperity. There is, at least in these papers in this time, little discussion about what is being studied and to what end - merely that high levels of education are necessary for continued improvement.

As is pointed out - both papers were written before 'learning analytics' was a phrase and though they clearly are referring to a similar process the absence of 'big data' on quite the scale as it is available today means that it's not exactly the same thing being discussed. However - the idea of analytics is clearly here with a vision to use data to improve polcy.

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Anna Greathead

Big Data and my favourite companies

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Costa Coffee is my favourite of the high street chains and I buy coffee from there about once a week on average. This article announces the companies intention to use 'big data'. The article is short of specific details but gives a few broad motivations behind the initiative. These are:

  • "to rapidly generate insights that create value for our business"
  • "provided more accurate decisions"
  • "significantly decreased the time required to understand the impact of each new idea"
  • "technology that can pinpoint cause and effect, allowing management to examine how their decisions alter the performance of their companies"
The detail is commercially sensitive but the big picture is that the behaviour of customers, branches, products and initiatives will be tracked, analysed and the results of the analysis used to make decisions.

Tesco is my main supermarket and the way it uses artificial intelligence and big data is described in this 2017 article. This article interestingly takes the angle that the way big data allows a company to anticipate, or even predict, the buying preferences of its customers is to be applauded and is appreciated by customers. It also describes how 'big data' is being used for supermarkets to regain control which was lost in price wars which left them less able to differentiate between the way customers interact with different brands based on factors other than price. As you would expect with any commercial enterprise the motivation is entirely commercial. Providing the customer with a better experience is only useful in so much as it may generate further spending and therefore greater revenue for the business.

There are 134,000,000 results for the Google search "Big data" Facebook. That's not surprising given the amount of data which Facebook have about their users, and the fact that they have 2 billion users. This 2018 article lists impressive figures about how much data is amassed and how quickly the data held is increasing. It makes the more obvious points about tracking activity of users but then adds these four less obvious ways in which the use of 'big data' can be observed:

  1. Tracking cookies: Facebook tracks its users across the web by using tracking cookies. If a user is logged into Facebook and simultaneously browses other websites, Facebook can track the sites they are visiting.
  2. Facial recognition: One of Facebook’s latest investments has been in facial recognition and image processing capabilities. Facebook can track its users across the internet and other Facebook profiles with image data provided through user sharing.
  3. Tag suggestions: Facebook suggests who to tag in user photos through image processing and facial recognition.
  4. Analyzing the Likes: A recent study conducted showed that is viable to predict data accurately on a range of personal attributes that are highly sensitive just by analyzing a user’s Facebook Likes. Work conducted by researchers at Cambridge University and Microsoft Research show how the patterns of Facebook Likes can very accurately predict your sexual orientation, satisfaction with life, intelligence, emotional stability, religion, alcohol use and drug use, relationship status, age, gender, race, and political views—among many others.
It then lists some features of Facebook  which are only possible because of 'big data' such as the flashback feature, the 'I voted' feature (which may be encouraging more people to vote) and services such as profile photo overlays to show support for various causes or events. 

Many of the ways in which Facebook uses big data seem benign and even fun. The platform uses the data it holds to remain engaging and keep the attention of its users. This ultimately makes advertising on the platform more lucrative and drives Facebook's profits.

A useful run down of how big data is used in other industries can be read here. Analytics have already changed our world. It seems likely that, as technology improves this process will accelerate.

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Anna Greathead

Learning Analytics according to Wikipedia

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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.

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