Understanding your digital educational environment as a student
Reading for this week: The data-doppelganger and the cyborg-self
It is necessary to choose a visual aid that is appropriate for the topic and audience.
It is easy to describe the digital environment we live in by listing the devices and platforms we use on a daily basis. In just the last 24 hours, I have used email, Telegram, WhatsApp, WordPress, Twitter, Blackboard, Teams, Office 365, and YouTube through an iPhone, iPad, MacBook Air, and iMac. Each of us likely uses 10, 20, or even 30 different digital services in a single day. However, it is more challenging to analyse why we use the particular combination of devices and platforms that we do. Is it personal preference, network effects (i.e. our friends/family use them) or institutional requirements (e.g. Blackboard at University of Manchester)? Our digital lives are not entirely self-determined but shaped by external factors.
Consider my own list - email is necessary for communication, as are messaging apps like WhatsApp and Telegram. I use WordPress because I have a long history with the blogging platform. Twitter is key for professional connections. Blackboard and Teams are required by my university to interact with students. And I use Apple devices due to ecosystem lock-in effects. The platforms we use quietly gather extensive data about our behavior through our daily use. After 7 years on WhatsApp, the platform has a detailed map of my contacts, messages, and more. This data shapes eerily accurate predictions about me by Google and others. Our digital environment may at first seem like a matter of personal choice, but on closer analysis is highly structured by social, institutional, and commercial forces.
Consider my own list - email is necessary for communication, as are messaging apps like WhatsApp and Telegram. I use WordPress because I have a long history with the blogging platform. Twitter is key for professional connections. Blackboard and Teams are required by my university to interact with students. And I use Apple devices due to ecosystem lock-in effects. The platforms we use quietly gather extensive data about our behavior through our daily use. After 7 years on WhatsApp, the platform has a detailed map of my contacts, messages, and more. This data shapes eerily accurate predictions about me by Google and others. Our digital environment may at first seem like a matter of personal choice, but on closer analysis is highly structured by social, institutional, and commercial forces.
This is the profile which Google had created about me when I checked it for last year's Digital Education Futures class. It led me to pause data collection on my Gmail and Youtube accounts out of concern for my privacy. Interestingly, when I checked again for this year's class it now said there was 'not enough info' to determine education level, relationship status, industry or home ownership.
But it has still collected an incredible amount of information about me over the years which could be used to map out every facet of my life in great detail, as well as how these have changed over time. If this were combined with other data sources (e.g. WhatsApp and Telegram, the University's e-mail data) it would offer an unsettlingly detailed picture of every facet of my personal and professional life.
Mapping your digital environment
Onsite students please come prepared to do this exercise in class. DL students please do this exercise yourself and keep a record of the results so we can discuss in our next tutorial.
- What devices have you used?For example a desktop, laptop, tablet, mobile or smart watch.
- What platforms have you used?For example e-mail, social media, video sites, learning management systems.
- Why are you using them?Is it purely a personal choice? What social and organisational pressures are there?
Network effects
The value of digital platforms depends on how many other people are using it. A taxi app that has few drivers is not useful, nor is a social media platform that none of our friends and family use. But network effects can also lock us into use by making it difficult to interact with others or access services unless we use a specific platform.
Organisational requirements
There are many platforms in education and work which are are forced to use as a condition of being a student or an employee. For example students at University of Manchester must use Blackboard and Outlook e-mail to keep in touch with teaching and administrative staff.
Datafication
Datafication involves turning aspects of education into digital data which is susceptible to (usually quantitative) analysis. This can involve the tracking of people in real time by processing data which is generated as a byproduct of digital interaction e.g. interaction on a learning platform.
Our digital environment is often shaped by forces outside of our control due to network effects and organizational requirements. For instance, we may be compelled to use certain platforms because our friends, family, school, university or employer have made that choice. These platforms then use datafication to measure our activity in order to influence our behavior. Commercial platforms generate data to sell targeted advertising and services. Educational platforms aim to improve the learning experience through data analytics. However, in using these mandatory platforms, our actions and behaviors are turned into quantifiable data without our full consent. The very act of participating in digital spaces cedes control over our data shadows. Even choices made for us result in new data doppelgängers that shape our online existence.
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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’.
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 organised
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.
Being cautious about big data
Several myths pervade the discourse around big data. There are claims that the data can speak for itself through statistical analysis without needing theories or models, that anyone with data skills can interpret it regardless of context, and that it ushers in a new era of purely inductive, objective science. Big data is portrayed as having an almost mystical power to reveal secrets if listened to properly. This echoes outdated notions like Galileo's 'Book of Nature'. However, the context for these myths is that many individuals, firms, and investors aim to profit from overhyping big data solutions. The actual capabilities of data science are obscured by excessive claims that 'data is the new oil' and a lack of clarity over definitions. Big data exhibits continuities with longstanding mathematical and statistical techniques, despite claims of radical novelty. Ultimately, the myths around big data often serve commercial hype more than a measured understanding of what insights data analytics can and cannot provide. There remain significant blindspots concerning biases and the need for human contextualization.
Educational research is not new, having involved surveys, ethnographies, and interviews for a long time. However, those methods intervene at a point in time and may influence what is observed or shared. The Dunning-Kruger effect shows people tend to overestimate their abilities when self-reporting. Big data promises to reveal what people actually do, not what they say they do to researchers. Collecting trace data from digital platforms enables unobtrusive research that does not directly interfere with subjects. This can provide insights distinct from traditional self-reported data. However, digital trace data has its own biases and gaps. Platforms shape user behaviors in particular ways. No single source of data can capture the complexity of education. Big data provides a new type of evidence but requires careful interpretation within the context of mixed-methods research. It complements rather than replaces more traditional educational data sources.
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. Her book Surveillance Capitalism is available in the library and there is an accessible overview by Ben Williamson here.
Nick Srnicek describes the rise of 'platform capitalism' in which powerful technology firms insert themselves as intermediaries into diverse aspects of social life. Platforms are multisided systems that enable interactions between different parties, as Uber does between riders and drivers or AirBnb between property owners and short-term tenants. In the 1990s it was predicted the internet would lead to 'disintermediation' with direct peer-to-peer transactions. However, we have seen the opposite arise with firms like Uber and AirBnb controlling huge swathes of activity. These platforms take a cut of financial transactions while accumulating valuable behavioural data they use to refine their services. They leverage 'network effects' where platforms become more useful as more people use them. Early leaders like Facebook or Amazon dominate their markets, making these 'winner takes all' economies. Rather than decentralizing commerce and society, platforms increasingly concentrate power and wealth in big technology companies.
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
Meeting your Data Doppelgänger
She has been nominated for an Academy Award, two Grammy Awards, and the Mercury Prize
Reconstructing your University of Manchester data doppelganger
1
What university services have you used so far?
For example the library, e-mail and Blackboard.
2
What could be learned about you from them?
Consider meta-data (e.g. when you used them and from where) as well as what you did with them.
3
What if this data was joined up?
Imagine if these different data sources could be joined together? What might the complete picture look like?
