Co-studied subjects in MOOCs

This post originally appeared on the HASTAC website at:

One of the great things about MOOCs is being able to study different subjects without having to have pre-existing qualifications or experience in those subject areas, or stick to a pre-defined choice of modules within a degree programme. But what courses do people choose to study together when these pre-requisites are removed? Do they stick to traditional disciplinary areas, or are new, inter-disciplinary areas emerging?

This is something which I became interested in through my early experiences as a student in MOOCs, and this post is about my first attempt at exploring it. By collecting information (only about courses – no personal information was included) from public profiles at Coursera, I created a network graph of co-studied subjects (each node in the graph represents a course; and if one person was enrolled on two courses, a link was created between them. And so on.).

You can browse an interactive version of the network graph here, and the work has been recently published as a short paper in the open-access International Journal of Emerging Technologies in Learning, which includes identification and characterisation of the communities of subjects which emerged. There was a good deal of overlap within the dataset, but a community detection algorithm (using Gephi) identified five communities. Broadly, these were characterised as: 1.Computer Science (pure) ; 2. Computer Science allied with Statistics/Economics/IT and others ; 3. Humanities allied with Social Sciences and Arts ; 4. Humanities allied with Business; 5. Natural and Physical Sciences.

The paper has some limitations in that it is a small dataset (but it is all the information that is available publicly) and it was collected last year – many more MOOC courses have run since then, so there may be an over-representation of the earliest courses. Nevertheless, it is a proof-of-concept demonstration of the kind of inquiry that can be made into MOOCs and interdisciplinarity. It would be great to repeat this work in the future to see how the picture changes.


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