A couple of really interesting talks today by some serious heavyweights in the learning, technology, and innovation fields: Stephen Coller (Gates Foundation), Ken Koedinger (CMU) & Ryan Baker (Columbia).
Stephen Coller opened the talks discussing how learning analytics can help us transform education and the challenges of bringing this to “market” (adoption in larger scales). Stephen noted that there is a common goal of most people in education to pursue the improvement of “the instructional core”. What I found particularly interesting is that he touted 3 key ways to really improve learning gains for students we need: (1) Change the rigour of the content that students are being asked to interact with; (2) Increase the knowledge and the skills of the teachers teaching the content; or (3) Alter the relationship of the student to the teacher and the content. Each of these are really powerful (but complex) means for making these changes and Stephen pointed out that changing one will probably have an effect on the other three as well.
He also noted the minerva project which approaches higher education through an interdisciplinary, real-world, authentic problem based curriculum. He noted that this form of learning may very well find its way into smaller college settings. This growing of authentic skills which are applied to real problems is really interesting and I could see it being a real motivator for students within such curricula.
Ken and Ryan did back to back talks on how they focus on what drilling deeply into student interaction and result data has told them about their designs and how to either improve them or to give direct insight into teaching and instruction practices. What really stood out here was the notion of “expert blind spots” – that we as researchers too often feel we know what we know (when in truth we might be missing critical factors or insights because we aren’t looking at the issue objectively). Instead Ken and Ryan showed how LA and data mining can reveal things about our curricular designs that we may not have seen (and could be adversely affecting our designs). Ken stated that “Intuitive design is not reliable”, and that careful analysis was more fruitful. I challenged him slightly on this noting that intuition is sometimes need in new or innovative designs (where we don’t have rich data from which to build on), he gave me one of the most memorable quotes of the conference “The hare of intuitive design and the tortoise of cumulative science”.