Autonomous vehicles or dashboards at #lak12?
Yesterday, at the #lak12 panel on learning analytics and educational mining, I tried to explain my view using a simple analogy:
- In my view, educational data mining work is more like the work on autonomous vehicles: the idea is to build algorithms that discover patterns in what learners do and then the algorithms can steer the learner in the right direction.
- On the other hand, the work we do on learning analytics is more about building dashboards that support people in being better drivers: the idea is to help learners to be more aware of what they do, support self-reflection and enable sensemaking.
The emphasis of much of this kind of learning analytics work is on empowerment, using computers to do what they do well (tracking and number crunching, so that we can for instance visualize what goes on in classrooms) in order to make people better at what we want people to be good at (be aware, reflect and make sense).
Seems like this distinction is at least one way to frame the distinction between LAK and EDM. I’d be very interested in your view…
Erik Duval's Weblog 
This is an interesting distinction, and one that I am grappling with at Kno. EDM can certainly help in particular situations such as math (something that Knewton is doing), but for using data generically, across the board for education and learning, LAK is a better paradigm. The metrics should help the student/learner guide them to a better outcome by making them aware of their progress in relation to their goals.