Week 4/5

Due to our missing class on October 13th, I have combined these pages to cover our sessions on October 20th and October 27th. The preparation requirements relate to our upcoming meeting on October 27th.

Join the group you will be working with on Teams for the rest of the unit.

Identify an educational technology to analyse for your case study.

Begin to discuss the provenance exercise.

The Digital Footprints we have mapped out in recent weeks help illustrate how ubiquitous data capture has become within the university. It is important to realise that the developments we are discussing are 'in here', within the university we inhabit, as well as 'out there' in wider society. If you can find ways to recognise datafication, digitalisation and quantification in your own experience (as a student in the university, as well as citizen in wider society) then it becomes easier to grasp how these abstract concepts relate to significant changes in education. But it might be helpful to recap these concepts first.

Large quantities of data are generated and collected on a daily basis, not only by and through schools, but also by (or in collaboration with) companies that are keen on gathering information from youngsters. Data sets range ‘from the often ad hoc “in-house” monitoring of students and teachers to the systematic “public” collection of data at local, state and federal levels’ (Selwyn, 2015: 66).

Jose van Dijck and Thomas Poell

Apple CEO

How It Works

1

Quantification

Data analytics depend upon information about educational processes being measured in a way susceptible to quantitative analysis. This can involve a substantial loss of meaning which it comes to things like the content of social media posts or online conversations.

2

Manual Analytics

This can be a manual process which gives instructors, programme directors or administrators data which can be used to infer outcomes. For example using engagement scores in the Canvas LMS to track engagement with the material and change how delivery of the material is organized

3

Automated Analytics

But it can also be automated which transfers professional judgement away from the teacher towards the system. This can be used to evaluate the teachers, as well as the students. For example In 2009-2010 school year the Washington school district in USA implemented a teacher assessment system called IMPACT which fired all those teachers who scores put them in the bottom 2%.

Shoshana Zuboff argues a new form of capitalism is emerging around 'big data'. What she terms 'behavioural surplus' (data generated by our behaviour through digital systems) is the new oil on which capitalism functions, comparable to how the expanding railway network needed coal. Datafication opens up new economic horizons by generate ever increasing quantities of behavioural surplus e.g. smart speakers, wearable computing/health devices and self-driving cars. This data is used to predict what we will do in future which can be used to sell products and services.

Case Study of Educational Technology tools

Choose an Ed Tech product or company that uses a tool capturing data for educational purposes
Explore the background to the company and consider the intentions of the tool, how the design of the tool reflects this background and what the drivers might be.
Search for more academic literature related to the tool or at least the application of the tool you have chosen.
Look at the website of the tool you are exploring for documentation and for any information about how it was designed and how users have been involved.
What claims can you make about this tool/product? What questions do you have about this tool/product?

Where can you find this information?

Web searching
Scholarly literature
Corporate websites
Corporate social media
Specialist media e.g. https://www.edsurge.com/, https://edtechnology.co.uk/, www.holoniq.com
News articles
YouTube videos
Documents for end users: T&Cs, policy documents, user guides (you might need to sign up for these!)

Case: what is the tool/platform/application you are exploring? What is the intended function of this tool?

Background: how was it funded? What stage is it at? Who is the target market? Who are the users? How does it make money?

Founders: who founded the company that started this product/service? What do we know about them? How do they imagine education?

Documentation: What documentation can we find about the use of data? Is there a data policy? Are security, privacy or other issues mentioned on the main website? Is there anything problematic in how this is phrased?

Interface: If we analyse the interface, does anything support what we have found in the other investigations? Does anything contradict this? How could we interpret these similarities and contradictions?

Analysing your corpus of documents

What is being claimed about the tool? E.g. with regards to learning & education, the use of data, the intentions behind the tool and outlook of founders.
Are different claims made with different audiences? E.g. are there differences between how the tool is presented to end users and to potential investors?
What impression is the company trying to create? E.g. how are images, colours, layouts, layouts combined in order to create a sense of identity for the company?
These are just suggestions! Please consider in your group what kinds of questions it makes sense to ask for this exercise.