 One example I've got here is all focused on how we embed student support and it works through our student support teams who have a fantastic dashboard that gives them information about the students who are in the various tutor groups across the UK. And we rely on the reporting that you can see here in front of you to develop interventions that will help support our students in their journey. So a very typical intervention here could be that we observe that the students do not hand in an assignment for example and so we're able to send them a message to say, please consider your assignment and should you be handing it in now. So we gently prompt the student. Another way in which we embed learning analytics is through the quality assurance and quality enhancement processes at the University. And typically qualification completion a whole range of different factors which help inform what things should enhance the quality learning experience of our students. And finally another example that we're very proud of are the principles that we've developed for the ethical use of student data for learning analytics. A real cornerstone to the Open Universities program around learning analytics is how we've gone about identifying at-risk students. So within the program we spent a lot of time very early on understanding just what the factors were that described our at-risk students. We need to look at demographic. We also have discovered that previous motivation and study patterns is very important so if a student has studied with us before or has done prior study they're more likely to be successful. Same with student progress in their previous OU study. Obviously the number of modules that a student take actually affects their progression and what qualification within that module as well. So there are more than 30 factors that underpin this that we know create the picture for the Open University of what a successful student looks like here. So we can basically tell where the student is going to pass or they're going to fail at key points within the module they're studying. Now all of the dots that you can see on the screen they're all represent aspects of the learning journey. They're learning activities. They're learning interactions. They're points within the journey within the learning design that we deem as important. And we can actually see which parts of that students touch and some parts of that journey have proved to be more successful for some students than other. So we're able to use OU Analyze to predict whether or not students are going to pass or fail but we use that information directly into practice. And what we've done is identified a series of modules which we can work specifically with module teams on to say hey we've noticed in this spot here in this learning activity but if students don't do this they're more likely to fail. However many students aren't doing that activity so what could we do to encourage them to do that. And that's just one example of the kind of intervention that we can make and how research and practice is actually at the heart of what we're doing in learning analytics.