The problem:
Libraries use a range of software systems through which users interact with premises, services and resources. The LMS system is far from the only source, the OPAC and the LMS circulation module representing increasingly partial views of user attention, activity and usage in a changing world. So libraries wishing to build a picture of user interactions face the challenge of identifying the appropriate data – depending on their purpose, which may range from collection management (clearing redundant material, building ‘short loan’ capacity) to providing student success performance indicators (if correlation can be established), to developing recommender services (students who used this also used that, searched for this retrieved that, etc).
Let’s split the problem down. In this guide we consider the variety of sources available within library services, a list to which you may add more. In other guides we consider strategies for deriving intelligence from ‘anything that moves’ as well as from targeted data extraction and aggregation with reference to specific goals.
The options:
Libraries already working with activity data have identified a range of sources and purposes – Collection Management, Service Improvement, Student Success and Recommender Services. Potential uses of data will be limited where the user is not identified in the activity (‘No attribution’). Here are some key examples:
Data Source | What can be counted | Value of the intelligence |
Turnstile | Visits to library | Service improvement, Student success |
Website | Virtual visits to library (no attribution) | Service improvement |
OPAC | Searches made, search terms used, full records retrieved (no attribution) | Recommender system, Student success |
Circulation | Books borrowed, renewed | Collection management, Recommender system, Student success |
URL Resolver | Accesses to e-journal articles | Recommender system, Collection management |
Counter Stats | Downloads of e-journal articles | Collection management |
Reading Lists | Occurrence of books and articles – a proxy for recommendation | Recommender system |
Help Desk | Queries received | Service improvement |
Taking it further:
Here are some important questions to ask before you start to work with user activity data:
- Can our systems generate that data?
- Are we collecting it? Sometimes these facilities exist but are switched off
- Is there enough of it to make any sense? How long have we been collecting data and how much data is collected per year?
- Will it serve the analytical purpose we have in mind? Or could it trigger new analyses?
- Should we combine a number of these sources to paint a fuller picture? If so, are there reliable codes held in common across the relevant systems – such as User ID?
Consider also the Guides on Student Success and Data Strategies
The Library Impact Data Project (LIDP) led by the University of Huddersfield - http://library.hud.ac.uk/blogs/projects/lidp/
To add to your data sources, you could also include Proxy server logs from systems like EZProxy as a source of activity data about e-resources. The RISE project www.open.ac.uk/blogs/rise has been looking at this data source in detail. It is certainly possible to create recommendations based on the data in the proxy server logfiles but to get the best out of the data it needs to be combined with student and bibliographic data.
ReplyDeleteLibraries increasingly seem to have access to Customer Relelationship Management systems which are a potentially rich source of data about user requests, although a limitation here is around how easy it may be to get access to the data.