OU blog

Personal Blogs

Picture of Anna Greathead

Ferguson 2012

Visible to anyone in the world

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.
Permalink
Share post
Picture of Anna Greathead

Big Data and my favourite companies

Visible to anyone in the world

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.

Permalink
Share post

This blog might contain posts that are only visible to logged-in users, or where only logged-in users can comment. If you have an account on the system, please log in for full access.

Total visits to this blog: 65607