Week 3

Welcome to the third week of the unit. Each week there will be a number of required tasks indicated at the top of the page for this week. It is important you attempt these before the seminar and read the material on this week's page.

Prepare a brief presentation about your student footprint timeline

Read Chapter 1 of Big Data in Education

Introduce yourself on Teams if you haven't already

We introduced Sir Francis Galton's work last week. The cousin of Charles Darwin is often called the father of big data, the inventor of questionnaires and statistical methods such as regression to the mean and also the inventor of eugenics. It should give us pause that the creator of methods so fundamental to many big data approaches had a mindset that was also fundamental to justifying a world war. Understanding more about intention is the thread that ties this unit together and we will think about Galton's intentions this week. In the video to the introduction to Galton in last weeks content, you will have heard the presenter say that Galton had some pretty reprehensible views. We focused on the positive side of those last week. Have a look at this video:

Your Data Doppelgänger will be made up of your browsing history, status updates, GPS locations along with a lot of other data that is being captured about you. This short video explains what a Data Doppelgänger is and how it is created.

This blog post from Mandy Pierlejewski explores data-doppelhangers in the context of early years education. This blog post is short and is based on her journal article, which is provided as an additional reading below. For more information about Data Doppelgängers and how this version of the self is created by the huge quantities of data collected about children and teachers please have a look at Mandy Pierlejewski's paper: The data-doppelganger and the cyborg-self: theorising the datification of education

(NOTE: this video is useful for information up to 2 minutes 30 and this is not an endorsement for this organisation).

Investigating provenance

This week explores the breadth of factors implicated in considering educational technology tools that are enabling Big Data approaches in learning, both formal and informal. This includes the mechanisms of capital behind Edtech. There is much excitement around investment opportunities in Edtech and the potential for developments in data analytics and machine learning. But the trajectories these projects follow can be led by commercial concerns over service and ethical concerns. So this week is a glimpse into how the commercial structures function which underpin funding for Edtech startups and initiatives and which in turn drive design decisions.

We'll be looking at how we might answer these questions:

Who owns the EdTech you use?
Who paid for the development of it?
Who designed it and who wrote the code to create it?
What was the original intent? Was it designed with an educationalist or commercial focus?
Has that intent manifested in the product you use?
How has the business model of developing the technology influenced its design and thus how you use it?
What social structures have been leveraged/exploited to justify continued funding for a product not yet monetised?
What implications does this have for the learning analytics that might be performed on this data?
What influence might that have in further development of the EdTech and policy and practice more generally?

We'll look at the basics of innovation funding, predominantly from a UK and U.S. perspective. We'll explore some critiques of the Edtech scene (the technical community that drive development of these technologies) from a historical perspective and then examine a particular case study of a recent and global Edtech startup, ClassDojo. We will consider a toolkit to analyse the design of a data capture tool and documentation around it. Finally we will consider designing your case study for the first assessment.

The Economics of Ed Tech

In this section we see some themes that arise from the way in which technology is funded. At each stage of funding rounds, or to garner support from larger organisations, educational technology projects have to justify their existence with revenue, projected revenue, or number of users, not with their impact on learning. Audrey Watters writes about the edTech development community from a critical and historical perspective. Her blog is well worth following, and at the link below, you can download as a pdf her "The Monsters of Education Technology' project.This isn't a book and has no page numbers, it is simply a collection of the transcripts of her talks as guest speaker during 2014. I would recommend all of it when you have the time, but for this week, the most relevant sections are The History of the Future of Edtech, which is on pp.8-16 of the pdf, and Ed Tech's Monsters on pp.75-85.
Audrey Watters, The Monsters of Education Technology.

In the former lecture Watters first suggests how the history of educational technology and that of computing is intertwined. She demonstrates the dangers of ignoring historical perspectives and says that the ideology of technology innovators is one which "shapes the story that many edTech entrepreneurs tell about education and about their role in transforming it". She asks what are the differences between current initiatives such as Khan Academy and Coursera, and equivalents from 15-20 years ago that are now ignored by the tech innovation community. She introduces some of the key players within Coursera and presents their comments on profitablity and views on education generally, and then looks to an Edtech that has taken hold in the same time period, the LMS or Learning Management System. Finally Watters talks about Seymour Papert and his aims for computers in education, to inspire new ways of thinking, rather than to instruct or deliver content, and she describes the commercialisation of PLATO, and its following failure, highlighting the parallels with online systems such as Coursera.

In the Ed Tech's Monsters section, Watters extends these ideas and shows the underpinning behaviourist approach, originating from B. F. Skinner, and valued by "libertarian tech types" who embrace a "free marketplace of ideas" and which has inspired gamification and persuasive design. The following two papers discuss the diverging views on the affordances of computers and technology which ought to be central to education, from Papert's vision of inspiration in the 1980s, which did not take hold, and Skinner's instructional, behaviourist vision, on which persuasive design is based.

The first paper will also be core reading in week 4. This is another from Ben Williamson, who analyses the Lytics Lab at Stanford University and Pearson's big data research centre in terms of the influence of their political economy on educational theory and the current trends in education data science. The considerable economic and social capital that supports these ventures gives them dominance over the market, and their perspective on education becomes encoded in the software tools they develop, black boxed and privileged over alternatives. He describes circumstances when technological capability enables a functionality that coincides temporally with a desire for that functionality - we'll see this in the case study also.

Williamson, B. (2017) Who owns educational theory? Big data, algorithms, and the expert power of education data science. In E-Learning and Digital Media, 14(3) pp.105-122.

Additional Reading

If you want to explore this topic further, this next paper parallels these issues with an exploration of why Logo, Papert's 1980 computing initiative, didn't take hold in formal education, which can be summed up by the following quote: "The gap between the initial expectations and the reality of its implementation demonstrates that the technology needs to be surrounded by social and political relationships that will allow it to do transformational work

Agalianos, A., Noss, R., & Whitty, G. (2001). Logo in mainstream schools: the struggle over the soul of an educational innovation. British Journal of Sociology of Education, 22 (4), 479–500.