Seminar by Lovisa Sundin: Educational Data Mining in CCSE
The task of optimising computer science education tends to be approached one randomised controlled trial at a time, with some grouping variables thrown in for good measure. The default analytical tool is a t-test or simple linear regression. Our focus on such costly but clear-cut comparisons – one experimental condition versus another – may blind us to other knowledge-generating opportunities, especially those provided by Moodle data footprints, as studied by disciplines like “educational data mining” (EDM) and “learning analytics”.
In this talk, I wish to provide something of a sweeping tour of different data analytical methods that are available to us, along with examples from the EDM literature. In preparation for the talk, please think deeply about what kind of data you have available and collect via learning management systems, and which research questions you potentially could answer through that data
First published: 1 June 2020