I find it somewhat personally disquieting that the course is already reaching the final stretch, and there are still so many things to absorb and learn! One of these new things is learning analytics, an emerging discipline concerned with Big data and algorithms as used in education.

As Ben Williamson’s presentation video pointed out, learning analytics should be seen within the context of the increasing pervasiveness of algorithms in society. To cite two examples among many, algorithms already influence how we find information online (Google PageRank) and how we interact with other people (Facebook News Feed) that it’s no surprise that algorithms have encroached on learning spaces as well. According to Williamson, the university is being seen as a data platform where data about learning activities are captured in order to both predict and prescribe learning outcomes. He also cautions however that data should be critiqued: data represents a sample, captures only a specific type of information, are interpreted through conceptual frameworks and they and they cannot be removed from wider social debates. Williamson concludes by talking about shifts within the knowledge landscape, one where algorithms and other non-human actors play an increasing role. This idea ties back to the previous weeks’ discussions on post-humanist education and assemblages.

This week I also enjoyed the tweetorial activity. Though my participation in the tweetorial was minimimal, it was good to be able to see quick reactions and responses from tutors and classmates, and as a group.