Learning Analytics: On Silver Bullets and White Rabbits

Learning Analytics sits at the intersection of Computer Science (drawing on sub-disciplines such as Data Mining, Information Retrieval, Information Visualization, Web Semantics) and Education (e.g. Educational Research, Measurement Science, Learning Sciences, Computer-Supported Collaborative Learning, e-Assessment). In my view it is an educational incarnation of Human-Centred Informatics (the effective design of human/digital information systems) and arguably Computational Social Science where social phenomena and computational modelling meet (elegantly introduced by Hannah Wallach in her recent Medium posting, and explored in relation to Complexity Science elsewhere). Extending several recent talks (e.g. EdMedia2014) this post introduces some of the big questions I see arising around the discourse of “educational big data.”

I was speaking at an event called Codes Acts in Education, and asked Siri to find me the website so I could check the agenda. Siri cunningly seized the moment to generate a pun on the very topic of my talk — the fears that many have around the impact should the automated algorithmic analysis of educational data take off.