Finding out about use

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

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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

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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