To browse Aalto University’s course selection, there is fairly new and concise site for that. One interesting novelty is recommendations, presented as a Related courses section at the bottom of the course page.
They allow harvesting, so I became curious on how would a course network look like. Are some courses more recommended than others? Which are they?
While working on this, I learned new things about Python both as a harvesting platform, and as a tool for constructing a directed graph in the form of a GEFX file. A nice example by Christopher Kullenberg helped here a great deal.
The color of the node reveals the School. The RGB values are taken from the Aalto University Visual Identity instructions.
Click a node, and an information panel opens up to the left.
Most of the nodes come with core metadata like title, number of credits, and description. If these are missing, it most certainly means that the harvester didn’t find anything because my Python code was too optimistic. Although the course pages are built with similar HTML elements and attributes, there do are exceptions. For example, some 50 course titles are not within an H3 element I realized. Because the harvest took more than three hours (!), I didn’t want to bother the site with a re-run. Those few nodes with a high amount of In-Degree links but without any course metadata, I edited manually in Gephi’s Data Laboratory before exporting the data.
I guess I could have done the GEXF modifications I needed within Gephi too but decided to brush up my dormant XSLT, once an everyday language at work due to frequent needs of XML transformations but today an exception.
So, which courses are recommended the most?
Number one is MUO-C3007, Design traditions, a bachelor-level course on the legacy of design, provided by the School of ARTS. The course is recommended by all other Schools of Aalto except SCI, School of Science. By hovering the cursor on top of the inbound links you can see how far, network-wise, some recommendations come from. I guess the design of physical artifacts follows similar historical traditions no matter what the realm of the final product is.
The second most frequently recommended one, not far behind MUO-C3007, is A23E53015 offered by the Open University, a masters-level evening course How to manage and assess the power of the brand (my translation).
Which courses are the most active in making recommendations of others, you might ask. Well, differences in rankings this way are hard to discern. Most courses recommend many others.
Jupyter notebooks, and XSLT code are available at GitHub.