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Analytical engines

December 11, 2013

Babbage never built his Analytical Engine… there’s a lesson right there.


The costs were too high, and the technology too primitive, and frankly the proposed end uses were really rather dubious.

We are in a similar sort of situation with Learning Analytics, in that the costs of implementing solutions are high, with technology that promises a lot, but won’t provide information in a format or structure that will allow data/stats illiterate users to make better than random decisions… and certainly provides potential to justify unethical decisions based on “financial prudence”

I attended the #CDEInFocus Learner analytics and Big data event in Senate House at the University of London yesterday. If you want an insightful review of the topics discussed read Myles’ blog:

Highlights for me were of course Adam Cooper of Cetis who gave a practical overview, and Doug Clow of the OU, who talked faster than me. Adam’s slideshare set says pretty much all you need to know:

Analytics is the process of developing

actionable insights


problem definition

and the application of statistical models and analysis against existing and/or simulated future data.

Doug looked at analytics through the experiences of MOOC participation and drop out, useful figures and pretty background pics:



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