Bill Jerome, in an excellent post aimed at people who perceive an obvious connection between learning analytics and Netflix recommendations:
The more a user stays engaged with [Netflix and Amazon], the more profit they generate. The comparisons to those kinds of analytics pretty much end there. Unfortunately for those looking for the easy path, our outcomes are complex and the inputs aren’t actually that obvious either.
Now what happens if we tell a student they aren’t achieving learning outcomes when in fact we are wrong about that? The potential for demotivating the student comes at a high cost. This could happen with errors in reporting the other way, as well. If learning analytics inform a student they are succeeding but in fact they are not prepared for their next exam or job, the disservice is just as bad. Getting learning analytics wrong on the learning dimension is a recipe for disaster and must be done carefully and with understanding.
As far as I’m concerned, between this post and Michael Feldstein’s earlier “A Taxonomy of Adaptive Analytic Strategies“, the e-Literate blog has cornered the market on nuance and insight in the learning analytics discussion.
BTW. Probably related: What We Can Learn About Learning From Khan Academy’s Source Code.