SIRTEL 2007
Social Information Retrieval in
Technology-Enhanced Learning
September 18, Sissi, Crete, Greece

Who?
• Riina Vuorikari, Katholieke Universiteit
Leuven & European Schoolnet, Belgium
• Nikos Manouselis, Informatics Laboratory of
Agricultural University of Athens & Greek
Research and Technology Network, Greece
• Erik Duval, Katholieke Universiteit Leuven,
Belgium & ARIADNE Foundation

Where? When?
• Sissi, Crete, Greece
– September 18, 2007
• In conjunction with 2nd European
Conference on Technology Enhanced
Learning (EC-TEL'07)
– September 19-20, 2007

Why?
\"We use people to find content. We
use content to find people.“
P.Morville, 2004

Background
• Social information retrieval (SIR) around
since the popularisation of WWW
• Refers to family of techniques that assist
users in obtaining information by harnessing
knowledge or experience of other users
– collaborative filtering, social network analysis,
social navigation, social bookmarking, …
– sharing queries, tags, annotations, ratings,
evaluations, …

In TEL
• SIR systems discussed and prototyped
in TEL since late ‘90s
• Manouselis et al., 2008 about 10 TEL
recommender systems
– lots more conceptual designs
– very few implemented & tested with real
users

A 2nd wave?
• Web 2.0 technologies & applications
seem to be bringing a new generation
of SIR systems into this discussion

SIR in TEL today 1/6
Collaborative tagging
• not anymore one metadata record produced
by one content expert…
• …but lots of annotational and attentional
metadata by lots of users

SIR in TEL today 2/6
Social bookmarking
• Reference to photos, books, links, music,..
• allows other users to navigate in personal
collections
• Similar resource-user-tag links
– recommendation purposes
– emerging social networks

SIR in TEL today 3/6
Expressing social ties
• not only seeing networks of friends
• professional, personal, recreational, etc
• Portability of these networks an issue
for better designs of tmr
– social-network-portability group
– PeopleWeb

SIR in TEL today 4/6
“Clicksteam” & user behaviour on the
Web not property of commercial
applications
• Attentional metadata a huge source of
information that is focused on now
– Contextual Attention Matadata, Attention
Profiling Mark-up Language, Attention
Trust, …

SIR in TEL today 5/6
Recommender systems introduced to
propose learning resources, events,
tutors, study-buddies to users
• Potential to play an important
educational role as well

SIR in TEL today 6/6
Content for educational use seeing a
change
• more & more user-generated content
on the Web
• Collaboration aspect is facilitated
by Web

To sum up…
• Social context is how we express
the who, where and with whom
• Social content are the objects or
digital artefacts that are in the
center of the communication,
exchange and networks

Agenda
• 09h30 - 10h00: Keynote MyStrands
• 10h00 - 10h30: Coffee
• 10h30 - 12h30: Annotation & Visualisation
Session
• 12h30 - 14h00: Lunch
• 14h00 - 16h00: Recommender Systems Session
• 16h00 - 16h30: Coffee
• 16h30 - 17h30: Discussion Session: Enablers &
Challenges for SIR in TEL

Lets start!

KEYNOTE
MyStands
• Music recommender
– And more... buy music, find likeminded, be
recommended, MyStrands.tv
• Francisco J.Martin (CEO)
• Jim Shur (Chief Architect)
• Rick Hangartner (Chief Scientist)

Next
Annotation & Visualisation
Session

Next
• 10h30 - 11h00:
– Analysis of User Behavior on Multilingual Tagging of Learning
Objects (Riina Vuorikari, Xavier Ochoa, Erik Duval)
• 11h00 - 11h30:
– Tag, Annotate, Rate and Share: Activites of Daily Living on
the Web (Shelley Henson, Justin Ball, David Wiley, Brandon
Muramatsu)
• 11h30 - 12h00:
– Reward structures for participation and contribution in K-12
OER communities (Griff Richards)
• 12h00 - 12h30:
– Visualizing Social Bookmarks (Joris Klerkx, Erik Duval)

Next
Recommender Systems
Session

Next
• 14h00 - 14h30:
– Recommendations for learners are different: Applying memory-based
recommender system techniques to lifelong learning (Hendrik Drachsler,
Hans G. K. Hummel, Rob Koper)
• 14h30 - 15h00:
– Exploring affiliation network models as a collaborative filtering mechanism
in e-learning (Miguel-Angel Sicilia, Salvador Sanchez-Alonso, Leonardo
Lezcano)
• 15h00 - 15h30:
– Simulated Analysis of MAUT Collaborative Filtering for Learning Object
Recommendation (Nikos Manouselis, Riina Vuorikari, Frans Van Assche)
• 15h30 - 16h00:
– System Demo: Knowledge Sharing in Information Seeking and Retrieval
Situations (Preben Hansen, Claus-Peter Klas)