 Learning analytics is using evidence about learners to improve the process. I think it's important because any important decision you make that's going to deal with basically people's lives, their education, you should do it with as much evidence as possible. You should do it based on facts. A lot of this knowledge is being gained by people over time like educators who have just picked this up naturally but not really formalized into what specific ways you can identify for particular students or particular courses how to optimize these for learning. We're trying to make something, some visualizations that use the course structure as a background so that instructors can see what parts of the course need more work or at what part students need more support. You can get students interested in these kinds of things early then it gives them an opportunity to explore them more at their time at UBC. It's still a really new field. There are not a lot of well-established examples of real systems in place in schools or colleges. The big effect I think is spreading awareness that there's more data out there and we should be using it. I hope that the Learning Hackathon will create conversations and they'll continue next week as many of the participants, we have students, we have instructors as many of these participants go back and talk to people. I hope that will spur these conversations and it will eventually lead to a culture of using data to make informed decisions. We can give people making educational decisions from the learners themselves to high-level senior administrators better evidence about what's actually happening with those students.