Tracking context with usage and attention metadata in
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Transcript Tracking context with usage and attention metadata in
Tracking
context
with
usage and attention metadata
in multilingual
Technology Enhanced Learning
Riina Vuorikari
European Schoolnet /
Open Univ. of the Netherlands
Bettina Berendt
KU Leuven
www.berendt.de
„Learning Object Repositories
foster re-use and improve
learning“
Really?
Ochoa (2008), Vuorikari & Koper (2009):
re-use ~ 20%
A basic challenge: context
Researching
for a
term paper
in ...
Biology
Political Science
Motivation
(application goals)
Use case
Technology Enhanced Learning / Learning Object Repositories
teachers from different linguistic and country backgrounds
Goal: foster re-use of resources
Assumption: understanding user context can improve
system behaviour
Approach:
consider UAM as part of context measurement, specifically:
Investigate different roles of social tagging
Profit from parallel research in different areas
Motivation
(general - workshop)
To exploit usage and attention metadata:
need to
define what properties of usage to use and
why
how to gather data on that property
what to do with that information
Motivation
(general – our work)
Automatically gathered indicators of context,
attention, ...
What‘s common across fields?
How can we communicate better?
What‘s specific in T.E. learning?
How can tags help learning & teaching?
How to bridge (e.g. lingual) barriers?
What are the best research methods for
answering all these questions?
Types of context
in TEL
What is context?
“Context is any information that can be used to
characterize the situation of an entity. An entity is a
person, place, or object that is considered relevant
to the interaction between a user and an
application, including the user and applications
themselves.” (Dey, 2001)
Any information not explicitly carried by the
“surface level“ of an interaction with a
computational system
Surface level:
user issues a query,
user accesses a resource, ...
We will distinguish between
Macro-context
Micro-context
Macro-context
educational level
formal and informal learning
delivery setting (distance, blended, ...)
intended users and user roles
...
Micro-context
User
trait
state
Interaction
atomic vs. activity structure
implicit vs. explicit interest indicators
Background knowledge / „semantic
enrichment“; often w.r.t. material
(Berendt, 2007)
Tags and context
What‘s in a tag?
item
user
• material
• user
• interaction
• „writing“
• „reading“
tag
Background
knowledge
Tags as interest indicators (1) –
or: measuring and using
interaction-context
Comparative search efficiency:
Search option [A|B|...] a target LO
Click-through rate aka confidence (fraction of successful
searches)
Path length
In LOR: Search with ...
explicit search (for resources w/o tags or ratings): 4.4 searches
/ resource
community browsing (tagclouds, lists, pivot browsing): 3.9
searches / resources
social information (community browsing or explict search with
interest indicators (tags, ratings)): 2.8 searches / resource
Recommendation of search options?!
Tags provide context to better
utilize background knowledge
Can tags be used for enriching existing metadata of
educational resources in a multilingual context?
30% of tag applications matched with descriptors from
a multilingual thesaurus, which had also been used to
index these learning resources (Vuorikari et al., 2009)
“Thesaurus tags”
Thesaurus tags provide for
adding properties to tags (e.g., relation to a concept in a
multilingual thesaurus, language)
linking resources to multilingual thesaurus descriptors
can support retrieval of resources in a multilingual context
Cf. convergence and quality tendencies in tagging in
general (Bollen & Halpin, 2009; Hayes et al., 2007)
What is user context – the
case of language
User models have many different variables
Language (first language, further languages,
proficiency, preferences): an important
descriptor of users
Language situation (interaction in a first or
second language): an important descriptor
Linguistic trait and state context variables!
Measures / indicators of
user language
self-profiling that explicitly addresses the question
(“your mother tongue?”)
IP address
browser settings
language of the currently used interface
language of search terms and tags
known or inferred language of tagged resources
NB: indicator of the language situation: compare user
language with interface/resource language (e.g.,
Berendt & Kralisch, 2009)
Tags as interest indicators (2) –
or: tags for measuring (re-)use
bookmarking and personal collections of digital
learning resources as a proxy for the use and
re-use of resources
When coupled with user and resource location
/ language:
become proxy for (re-)use across national and
language boundaries
(e.g., Vuorikari & Koper, 2009)
Why support trans-lingual reuse? (1): Situation on the Web
What would be an „ideal“ language situation on
the Web?
One H0: ~ amount of materials proportional to
number of first-language speakers of a language
Not the case – non-English languages are
severely under-represented on the Web due to
Resource-creation behaviour
Link-setting behaviour
Link-following behaviour
Attitudes towards resources in different languages
(Berendt & Kralisch, 2009)
Why support trans-lingual reuse? (2): User preferences
Theoretical reasons (cognitive effort), supported by
implicit and explicit user preferences:
Clear tendency to access materials in one‘s mother
tongue in a medical information system
17% of users of a LOR had saved only content in their
native language in their Favourites
LORs: pragmatic reasons (curricula)
Search-engine give better results when queried with
linguistically correct (rather than Latinized) spellings
But: individual differences depending on proficiency in
English!
(Berendt & Kralisch, 2009; Vuorikari & Koper, 2009; Blanco & Lioma,
2009)
Using context for improving
trans-lingual re-use: How? (1)
better-organised search result lists
first a ranked list in the preferred language,
then a ranked list in a second language, etc.
done: Google for general search results and
by LeMill (lemill.net) for learning resources.
organise tag clouds by language or
country as in LRE (lreforschools.eun.iorg)
Using context for improving
trans-lingual re-use: How? (2)
for users with weaker language preferences; for
„unknown“ users; ...“:
Make exceptions to the foregoing by recommendations
using
travel-well tags:
terms with the same or similar spelling in most languages
ex.: technical terms like “mathematics”, place and person names
How to identify them?
via multi-lingual thesaurus or similar
tag has been assigned by users from different languages
Tag has been assigned to resources in different languages
travel-well resources:
Have been bookmarked by people from different languages,
country contexts, …
(Vuorikari & Ochoa, 2009)
Using context for improving
trans-lingual re-use: How? (3)
Direct recommendations 1 (see above):
Recommend content or tags in a preferred language
Indirect recommendations:
E.g., recommend bookmark lists of other users with a similar
“language preference profile”
Profile similarity: “extensional” and/or “intensional”: degree of
tolerance for mixed-language resources, results, etc.
Direct recommendations 2:
repositories could specifically encourage users who are
competent in “smaller” languages also to author content in
their language.
Outlook
Some questions
... and now yours! (Thx!)
How to best couple „content“ and „architecture“
research
How to bring together research from Web analytics / eCommerce, search-engine research, digital libraries,
TEL, ...
How to complement the exploratory research with
experimental research effectively and efficiently
How to best combine different methods (validate that
the dependent variables used mean something!)
Which privacy/security questions arise?
What is specific for TEL?
References
Most references can be found in the paper, available at
www.cs.kuleuven.be/~berendt/Papers/TEL_context_attention_vuorikari_berendt.pdf
In addition:
Bollen, D., & Halpin, H. (2009). An Experimental Analysis of
Suggestions in Collaborative Tagging. In Proc. Of WI-IAT’09, Milan,
Italy, 15-18 Sept 2009. IEEE Computer Society Press.
Hayes, C. et al. (2007). ... In Berendt, B., Hotho, A., Mladeni\v{c}, D., &
Semeraro, G. (Eds.) (2007). From Web to Social Web: Discovering
and deploying user and content profiles. Workshop on Web Mining,
WebMine 2006, Berlin, Germany, September 18, 2006, Revised
Selected and Invited Papers. LNAI 4737. Berlin etc.: Springer.