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First year History students
searching for Napoleon
T
I
L
E
David Kay
Also borrowed …
They downloaded …
They rated this resource as …
They also recommended ...
Sitting on a goldmine
- the value of
attention and activity data
Supermarkets
“Supermarkets gain valuable insights into
user behaviour by data mining purchases
and uncovering usage trends.
Further insights are gained by analysing
purchasing histories, facilitated by the use
of store loyalty cards.”
Dave Pattern, University of Huddersfield – TILE Workshop – December 2008
Libraries?
“Libraries could gain valuable insights into
user behaviour by data mining borrowing
and uncovering usage trends.
Further insights are gained by analysing
borrowing histories, facilitated by the use
of library cards.”
Dave Pattern, University of Huddersfield – TILE Workshop – December 2008
Types of ‘Attention’ Data
An attempt to break down the potential sources
•Attention
•Click stream behaviour indicating interests / connections
•queries, navigation, details display, save for later
•Activity
•Formal Transactions
•requesting, borrowing, downloading
•Appeal
•formal and informal lists
•a type of recommendation
•can be treated as a proxy for activity?
•And …
We could
concentrate and contextualise
the intelligence (patterns of user activity)
existing in HE systems at institutional level
whilst protecting anonymity
in order to
deliver ‘web scale’ services
of value throughout the community –
to undergraduates & researchers,
to lecturers & librarians,
to the institutions themselves.
TILE Pain Point
Deriving Context
My
Studies
My
I.D.
From VLE or
Registry?
LMS/VLE/etc
Click streams
Modules from
VLE or VRE
My
Networks
e.g. FaceBook
Subject
Networks
My
Activity
My
Context
My
Responses
Bookmarks
Reviews
& Ratings
Not in initial
specification
My
Publications
User controlled
HE ‘controlled’
Automated
Academic
Standing
My
Parameters
My
Interests
Keywords
Incl. Location
& Override
The possibility of
critical mass of activity data from ‘Day 1’,
brings to life the opportunity & motivation
to embrace and curate user contribution
(including ratings, reviews, bookmarks, lists)
Barriers to contribution
& use of contributed information
must be as low as possible TILE Pain Point 2
Library 2.0 ‘Catalogue Related’
Activity Examples
Enabling Contribution
Metadata only
Benefits of contribution
must be clearly visible
with real promise of
being useful
Metadata + Resource
Create
Local
Catalogue
Author
Discover
BL
WorldCat
Authorise
Search
Locate
Approve
Validate
SUNCAT
OER
Mash
Reuse
Intute
Deliver
Enhance
Access
Request
Catalogue
Tag
Repositories
COPAC
Publish
Expose
Liberate
Consume
Rate
Review
Use
Archives
Recommend
LibraryThing
Local
VLE
Curate
Sustain
Persist
Business Processes
Google
Scholar
My Website
Services
Distributed …
Content & finding aids
anywhere & any type
What’s
recommende
d in the VLE?
Concentration of …
Context data
Catalysing contribution
Did anyone
highly rate a
textbook?
What’s did last
year’s students
download
most?
Across …
An Institution
A Consortium
A national system
Global communities
What’s the
economics take
on this topic?
What do
undergraduates
elsewhere read?
California
State
University
2008
MESUR
contains 1bn usage events (2002-2007) obtained
from 6 significant publishers, 4 large institutional consortia and 4
significant aggregators!
The collected usage data spans more than 100,000 serials
(including newspapers, magazines, etc.) and is related to journal
citation data that spans about 10,000 journals and nearly 10
years (1996-2006).
In addition we have obtained significant publisher-provided
COUNTER usage reports that span nearly 2000 institutions
worldwide.
The data is being ingested into a combination of relational and
semantic web databases, the latter of which is now estimated to
result in nearly 10 billion semantic statements (triples).
MESUR is producing large-scale, longitudinal maps of the scholarly
community and a survey of more than 60 different metrics of
scholarly impact.
MESUR
Personalisation > Aggregation?
‘The more we track and aggregate,
the more our suggestions will be personalised.’
‘… and the more we track, the better
we can adapt our service without your intervention.’
‘… we’ll even learn
to recommend content
by taking account
of your location,
habits & moods
and by
making comparisons’
My Calendar
My GPS data
My activity patterns
WP1
April
WP2
A2
A1
Business
Options
HEIs
Vendors
Data Analysis
& Model
WP3
LMS, ERM, VLE sources
Dataset
Extraction
May
WP4
June
mosaic
Grant Awards
B1
Search
Demonstrator
Mimas
Scale, Facets, Sense
July
August
WP5
H’field
Dissemination
Library, LT &
Developer
Community
C1
September
October
November
A1 etc = TILE
Recommendations
Conferences
Workshops
Competition
Website
Librarians
WP6
User Demand
Research
Mimas
CERLIM
Recognition, Value
C1/Footnote
Professional
WP7 Opinion
Integrity, Value
B2/Footnote
WP8
Triangulation
& Forward
Recommendations
The No.1 Challenge - Generating Data
Institution
LMS
Commitment
University of Dundee
Ex Libris Aleph
committed to supplying data
University College Falmouth
Ex Libris Voyager
confirmed interest in supplying data
Swansea University
Ex Libris Voyager
confirmed interest in supplying data
University of Warwick
Innovative (Millennium)
interested but not possible with current LMS
University of Huddersfield
SirsiDynix Horizon
data supplied
University of Lincoln
SirsiDynix Horizon
committed to supplying data
University of Wolverhampton
Talis
data supplied
University of Sussex
Talis
committed to supplying data
University of Greenwich
Talis
recently invited to supply data
University of Sheffield
Talis
interested but cannot commit to supply data
Some have the transactions
Some have the links
Some have the technology
Some have the resources
Thanks to
library teams and
individual pioneers
for their engagement
Six entries to our recent competition
to build applications around activity data
Using multi-year released by the University of Huddersfield
• Improving Resource Discovery
– An intuitive interface to navigate the ‘Book Galaxy’ through
links based on mass borrowing habits
– Users create reading lists and share with other students
(and lecturers)
• Supporting learning choices
– Applicants or new students get a feel a course based on
the books students actually borrow
– Possible courses of study are suggested based on the
ISBNs of books you’ve personally enjoyed reading
• Supporting decision making
– Collection managers visualise historic circulation data
relating to courses of study
– Value the loans related to a specific course as a collection
performance indicator
Some Questions
• What range of data sources available within higher education
should be used to derive activity and context?
• Does activity data need to be aggregated above the
institutional level to achieve web scale and network effect?
• Amazon tells you that ‘people who did this also did that’. Can
academic libraries offer something more significant (‘people
LIKE YOU who did this also did that’) because they know the
user’s context (typically their course and institution)?
• Precision in such as metadata and even citation is subject to
personal judgements and motivations. Are these less reliable
than pointers derived from mass contextualised activity data?
• As proxies for real activity, are lists – formal reading lists,
informal student lists – a form of attention data which can be
highly weighted?