My studies last week were interrupted by the Presidential election in the USA. Like millions of others, I spent Tuesday evening checking the results as they were first broadcast by the TV Networks. It soon became clear that, not for the first time, the pollsters got it wrong.
As Selwyn reminds us in Data entry: towards the critical study of digital data and education data in its digital form are now being generated and processed on an unprecedented scale (p.64). But is big Data the panancea it is often presented as?
In an age when we have access to more data than ever, how useful is this data?
As discussed in the TED Radio Hour episode Big Data Revolution, data is everywhere. But what is the value of Big Data? And which metrics do we overvalue and which do we undervalue? What does this tell us about ourselves?
If we are to maximise the possibilities of Big Data we must first acknowledge that data can be a blunt instrument. As data analyst Susan Etlinger says in the episode ‘data doesn’t create meaning – people do’. We therefore need to spend more time on our critical thinking skills. An important question Etlinger raises is: did the data really show us this? Or does the result make us feel more successful, or more comfortable?
All core readings for this week explored various considerations around what Big Data means for Education. In The rise of Big Data: what does it mean for education, technology, and media research? (2013) Rebecca Eynon argues that ‘as a community we need to shape the (Big Data) agenda rather than simply respond to the one offered by others’ (p238) and offers three areas requiring particular attention:
- what are the ethical considerations surrounding Big Data? Eynon offers a clear example in the shape of using data to predict drop-out rates. If an institution calculates a particular student is likely to drop-out, what do they do with that information?
- what data do we have? we can only study data we have or we can collect, therefore the (limited) data we have restricts what we can research (including inferring meaning).
- how Big Data can reinforce and even exacerbate existing social and educational inequalities.
Eynon also raises the challenge of how we train (future) academics in this field to ensure ‘we use these techniques to empower researchers, practitioners, and other stakeholders who are working in the field’ (p.240). This point is echoed in Learning in the Digital Microlaboratory of Educational Data Science where Ben Williamson references Roy Pea (Stanford University) who has called for a new specialised field in this area and identifies “several competencies for education data science”. The report also calls for ‘new undergraduate and graduate courses to support its development’.
Williamson then goes on to discuss the educational publisher and software vendor Pearson and their Centre for Digital Data, Analytics and Adaptive Learning. Digital microlaboratories such as these ‘relocate the subjects of educational research from situated settings and psychological labs to the digital laboratory inside the computer, and in doing so transform those subjects from embodied individuals into numerical patterns, data models, and visualized artefacts’. What nuances are lost in this?
I was interested to learn of the startup schools Williamson refers to (AltSchool, Kahn Lab School, the Primary School) which utilise ‘data tracking and analytics to gain insights into the learners who attend them, in order to both “personalise” their pedagogic offerings through adaptive platforms and also test and refine their own psychological and cognitive theories of learning’.
Also of interest was how Pearson has partnered with Knewton to create The Newton Adaptive Learning Platform which uses proprietary algorithms to deliver a personalized learning path for each student.
This reminded me of Todd Rose’s presentation at TedX on The Myth of the Average. It also reminded me of German Chancellor Angela Merkel’s recent warning on the dangers of the potential of proprietary algorithms to narrow debate.
The paper which discussed in most detail the implications for Big Data in Education was Selwyn, N. 2015. Data entry: towards the critical study of digital data and education. Learning, Media and Technology. 40(1). Again, we are reminded that ‘as with most sociological studies of technology, [these] researchers and writers are all striving to open up the ‘black box’ of digital data’ (p.69). Digital sociologists don’t see data as neutral, but rather inherently political in nature. But ‘data are profoundly shaping of, as well as shaped by, social interests’ (p69). Selwyn argues that educational researchers therefore need to be influencing this new area of sociology. What role is digital data playing in the operation of power? (How) does it reproduce existing social inequalities? How does it reconfigure them?
A key question to ask is therefore ‘who benefits from the collection of this data in education contexts’?
Data surveillance (dataveillance) supports data profiling and crucially, ‘predictive’ profiling (p.74) (echoing Eynon’s point about predicting college drop-outs). Digital surveillance is of course helped, and perhaps made more transparent by the increasing use of VLEs in educational contexts. Whilst this is often framed as an opportunity to evaluate the effectiveness of different aspects of a course, this heightened transparency can lead to ‘coded suspicion’ between academic staff, administrators and students (Knox 2010).
In addition to creating suspicion, analysing data is inherently reductive. Nuanced social meaning is easily lost when data is presented as discrete and finite. We therefore need to consider specifically what reductions must we consider in relation to education. Selwyn argues that firstly we must acknowledge that we tend to measure what we can measure most easily. In an education context this means we measure attendance, student satisfaction and assessment results – all of which can be crude instruments.
Finally, all educational researchers need to be familiar with a variety of data tools and analytics models. In his conclusion Selwyn argues that we need to refuse to take digital data ‘at face value’ but rather recognise the ‘politics of data’ in education and act against it (p.79).
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Key questions to ask in relation to Big Data in Education
- what data do we have?
- what data do we need?
- how is the data collected?
- how does the harvesting of data affect relationships between faculty, administrators and students?
- who benefits from data collection?
- to whom is the data being made available?
- who is collecting data in education?
- what skillsets do data researchers need to better understand data?
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