Active Algorithms: Sociomaterial Spaces in the E-learning and Digital Cultures MOOC

This paper will explore two examples from the design, structure and implementation of the ‘E-learning and Digital Cultures’ Massive Open Online Course (MOOC) from the University of Edinburgh in partnership with Coursera. This five week long course (known as the EDCMOOC) was delivered twice in 2013, and is considered an atypical MOOC in its utilisation of both the Coursera platform and a range of social media and open access materials. The combination of distributed and aggregated structure will be highlighted, examining the arrangement of course material on the Coursera platform and student responses in social media. This paper will suggest that a dominant instrumentalist view of technology limits considerations of these systems to merely enabling or inhibiting educational aims. The subsequent discussion will suggest that sociomaterial theory offers a valuable framework for considering how educational spaces are produced through relational practices between humans and non-humans. An analysis of You Tube and a bespoke blog aggregator will show how the algorithmic properties of these systems perform functions that cannot be reduced to the intentionality of either the teachers using these systems, or the authors who create the software, thus constituting a complex sociomaterial educational enactment

Refocusing Zuboff’s ‘division of learning’ on education

This paper examines the concept of the ‘division of learning’, and the broader thesis of ‘surveillance capitalism’ within which it is situated, in terms of its relevance to education. It begins with defining the term, before suggesting two key ways in which aligning the ‘division of learning’ with perspectives from educational research might provide productive insights for both domains. The first considers the impact of increasing ‘datafication’ in education, where platform technologies are proliferating as powerful actors that both mediate and shape educational activity. Here the ‘division of learning’ offers useful insights concerning the disparities resulting from learning in and learning from educational platforms. The second explores the extent to which education theory might offer ways to develop the concept of the ‘division of learning’, through critique of the term ‘learning’ itself, as well as the foregrounding of questions of educational ‘purpose’. Here the ‘division of learning’ is suggested to maintain, rather than challenge, the dominant practices of data exploitation, for which further engagement with a purposive, political, and emancipatory form of ‘data science’ is suggested