1. Finding out about use CAUL 7 April 2006

2. Quick summary This is about using information we generate to find out about use. Why? Hard data about complex changes Output data not input Impacts and outcomes Understanding the customer A cure for folklore

3. Information resources Analog – loans can be tracked to classes of people, e.g. full-time / part time, faculty, undergraduate / postgraduate e.g. active user data Digital - who uses what? how can use be tracked to people? E.g. EZProxy compared with, COUNTER data.

4. Computer use Most universities now require signon We can track –Hits –Logins – e.g. Kinetica – who uses it? –sessions We can track the activity of –Classes of users –Computers, spaces, laptops

5. Use of space Traffic counts are invaluable –Movement in and out –Movement within –Use of a service desk –Bookings for spaces e.g. TrafficProX can be used in many contexts and data brought together

6. Training The question is how do we classify it? We all keep statistics of classes and numbers of people E.g. orientation/introduction, specific software, specific products, particular skills (e.g. research)

7. Asking questions Again, the issue is how we classify them – number counts are of no value, since we can already get a people count automatically What categories of use do we use. E.g. the Swinburne experience