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: http://myles.jiscinvolve.org/wp/2013/12/10/740/
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
through
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:
