III

Recoding Education

Manifesto Points Covered

Figure 3.1

Introduction

Part III connects, by way of critical analysis, four points made within the Manifesto for Teaching Online and four phenomena currently prominent within digital education: the open education movement, massive open online courses (MOOCs), algorithmic functions in education technology, and automation. These areas have received much attention in recent years, and here we provide background to the manifesto points that challenge some of the dominant assumptions that surround them. Both open education and data-driven technologies have promised significant disruptions, proposing visions of egalitarian access to educational opportunities and new kinds of scientific precision in how we “do” teaching. While these ideals have merit, it is important to look beyond the hyperbole and attempt to understand the broader political, social, and economic contexts in which they are situated.

First, open education is examined through two examples that provide the means to examine how openness is portrayed in education: the Year of Open proposed in 2017 by the Open Education Consortium, and the OER World Map project funded by the Hewlett Foundation. The chapters in this part focuses on interrogating the assumptions of neutrality and inherent value that too often accompany the discourse of open education and offers a number of critical perspectives through which scholarship and practice in this area might be developed. We argue in chapter 11 that Openness is neither neutral nor natural: it creates and depends on closures.

Second, this part examines the recent proliferation of open courses, often aligned or associated with scale and the MOOC. The enrollment numbers in these courses, routinely emphasized in media hyperbole at the time when they emerged (around 2012–2013) aligns, we argue, with a negative framing of openness that assumes a narrow definition of educational access. The attempt to globalize education through such courses, we suggest, enacts a problematic kind of standardization and promotes a corporate vision of the rational, self-directing learners we discussed in part I and which continues to shape how we understand open education. The chapters in this part call for a greater focus on the capacity of open online courses to encompass difference. Massiveness is more than learning at scale: it also brings complexity and diversity.

Third, aspects of open courses are connected to the routines of data collection and processing that underpin the technologies involved in delivering them. Drawing on the critiques of instrumentalism in education technology discussed in part I and on emerging work in critical algorithm studies, this analysis highlights the ways analytic technologies are deeply entangled in educational practice and learner activity. We argue that the opaque inner world of algorithmic operations recenters educational power and agency through a kind of technoscientific governance. Algorithms and analytics recode education: pay attention!

Finally, this part examines the increasing automation of educational activity, and specifically teaching, through the use of data-driven technologies. Automation is examined as a key site for critical attention, given the prevailing economic rationale underpinning the notion of replacing particular teaching functions with precise and efficient machines described in part I. However, we also suggest that automation, used carefully, has the potential to offer a new source of critical, creative pedagogy, where alternative approaches built around the possibilities of excess and playful critique can be explored. Automation need not impoverish education: we welcome our new robot colleagues.