Group Admins
Description
Session Description
In a world of “smart” appliances and constant surveillance, teachers and learners can’t engage in digital spaces – socially or educationally – without having to deal with questions of data. Whether as web tracking or as analytics collected by institutional systems, datafication surrounds us in higher education, and our knowledge-making and knowledge-dissemination systems are rife with datafication implications (Williamson, 2018). But do those of us who work in open education and so-called higher learning understand these implications? And if we don’t, what does that mean for the relationship between higher education and knowledge?
Through this hands-on Open Space conversation, facilitated in part via sticky note / post-it tension pair activities, we will explore the challenges datafication poses for educators in our contemporary information ecosystem, and why all of us should care. The session will scaffold frameworks that offer participants a critical lens to analyse their own data literacies and explore pathways to data literacy and data activism in institutions and networks. Participants will contribute – with overt GDPR-compliant consent – to data visualization activities that will serve to guide future research into educators’ data literacies. Remote participants will be able to contribute to an open hashtagged conversation on Twitter and share their input as well, and slides and session leaders’ follow up reflections will be shared openly on the web.
The session will open with a brief overview of datafication and why it matters, and to whom. We’ll examine the premises and promises of big data, as well as the limitations they place on learning. We’ll explore the concerns regarding surveillance, bias and exclusion connected to data-driven practices that are beginning to emerge in scholarship (Zuboff, 2015; Noble, 2018; Gilliard & Culik, 2018) and in popular media (Brown, 2017; Schwab, 2019), as well as the urgent question of what to do about non-governmental platforms, such as Facebook, that wield society-wide powers.
This Open Space “lab” is based in the authors’ research into data literacy and faculty development. We’ll showcase our study on how the concept of data literacy circulates in contemporary literature, and then engage participants in reflective data visualization of practices and possibilities. We’re particularly interested in exploring participants’ perceptions of the relationship(s) between open educational practices and data literacies, so activities will address students’ data and open data within open education contexts. Our aim is to build together toward more complex and critical understandings of datafication among educators, particularly open educators, in this age of surveillance.
References
Brown, M. (August 30, 2017). Math isn’t biased, but big data is. Forbes Magazine. Retrieved from
https://www.forbes.com/sites/metabrown/2017/08/30/math-isnt-biased-but-big-data-is/#6eb1f154d564
Gilliard, C. & Culik, H. (2018). The new Pythagoreans. boundary2org. Retrieved from
https://www.boundary2.org/2018/07/gilliard-culik/
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism.
NYU Press. https://doi.org/10.15713/ins.mmj.3
Schwab, K. (January 6, 2019). IoT security is so bad, many companies can’t tell when they’re hacked. Fast Company. Retrieved from
https://www.fastcompany.com/90292568/iot-security-is-so-bad-many-companies-cant-tell-when-theyre-hacked
Williamson, B. (2018). The hidden architecture of higher education: building a big data
infrastructure for the ‘smarter university.’ International Journal of Educational Technology in Higher Education, 15(1), 12. https://doi.org/10.1186/s41239-018-
0094-1
Zuboff, S. (2015). Big other: Surveillance capitalism and the prospects of an
information civilization. Journal of Information Technology, 30(1), 75–89.
https://doi.org/10.1057/jit.2015.5
