Data and Education – summary
![](https://blogs.ed.ac.uk/s2139617_an-introduction-to-digital-environments-for-learning-20202021sem2/wp-content/uploads/sites/3908/2021/03/data-ed.jpg)
Here below are 2 summaries of the 4 data and education questions that we were posed during the week. I have included a short answer of my own and a few tweets to give a flavour of the debate.
Eynon, R. (2013). The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology, 38(3), pp. 237-240.
Williamson, B. 2017. Introduction: Learning machines, digital data and the future of education (chapter 1). In Big Data and Education: the digital future of learning, policy, and practice.
Knox, J., Williamson, B. & Bayne, S. 2020. Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology,
That’s a really nice presentation. There’s some useful comments and reflections here about the role in education students for the data society and two the data society shapes the practices of education. Your point on different perspectives and being clear on spelling out your terms is an important one. Also, the purpose of selecting, collecting, collating and analysing data is a key issue – you could argue that there is no data without a purpose, but some purposes are not made explicit or unconscious but still there and identifying such purposes or effects of particular data selections is a key part of critical data studies – if you are collecting data on education, you must have some model of what education is in mind, otherwise identifying what is and is not education data is impossible (never believe a data scientist who claims ‘the data just speaks for itself’). That feeds in to you other comment citing Eynon that research (analysis) is shaped by what data is available – it reminds me a supposed saying form the McKinsey management consultancy that “everything that is measured can be managed” and then, more threatening “and everything can be measured”. I like your reference to Knox’s idea of a pre-data ‘fog’ – are we now generating a new fog of datafication that is obscuring what matters? An interesting post with a good layout that works really well.