Supporting Privacy in E-learning with
Semantic Streams
Lori Kettel, Christopher Brooks, Jim Greer
ARIES Laboratory
Advanced Research in Intelligent Educational Systems
Computer Science Department
University of Saskatchewan
Saskatoon, SK, Canada
Overview of Presentation
1. Background
–
E-learning, Computer Supported Collaborative Learning
(CSCL), User Modelling, AI-Ed, and Learner Privacy
2. Empirical Work
–
User views on privacy in e-learning
3. Our systems
–
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An architectural solution to the need
An implementation that supports this solution
4. Work in progress
–
Integrating privacy with a generic user modelling
component, is this impossible?
Copyright © 2004.
Why are we here
• We’re interested in intelligent e-learning – building
learning environments that:
–
–
–
–
understand a users’ goals
understand a users’ background knowledge
are able to automatically adapt content to fit a user
are able to connect users into meaningful collaboration
• These learning environments are distributed in
nature
– Different applications support different activities
– E.g. LCMS, live chat forums, asynchronous forums,
electronic submission/feedback systems, etc.
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Steps along the way
• One such application is I-Help, a public forum
discussion system and instant messenger
– Available to all undergraduate computer science
students at the University of Saskatchewan
– Allows learners to request and provide help
– Learners indicate their proficiency in a subject,
and I-Help provides for expertise location
– Each user had own personal agent
– Agent designed to protect and share information
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Initial Privacy Server (PEST)
• Application agent that talks to the I-Help
databases and to the personal agents
• An agent that sits between the application
datastores and users’ personal agents to
control the flow of information.
• Personal Agents tell PEST what information
is allowed to be released.
• Personal Agents request information about
others through PEST.
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PEST
Server
Data
store
I-Help
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Evaluation of PEST - in learner awareness task
• 32 participants – 18 computer science and 14 nonscience students
• Initial Privacy Preferences
– >50% would reveal information using alias or as part of a
summary
– Three would not hesitate to reveal all types of information
using their name
– Three would conceal absolutely all information about
themselves (non-science students)
– No significant difference between computer science and
non-science students
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Evaluation - Final Questionnaire
• 78% felt awareness of other learners may or would
give a better sense of community
• 78% felt it may or would make the system more
personalized
• 88% felt the awareness tools may or would be
beneficial
• 88% felt the awareness tools may or would be a
privacy risk
• 88% felt the potential benefits may or would be
greater than the privacy risk
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PEST Pros and Cons
• Pros
– Allowed users fine-grained control over their
information
– Promoted awareness of other users in the
system to facilitate learning and a sense of
community
• Cons
– Required detailed ontological domain knowledge
– Only developed to work within I-Help
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Current e-learning landscape
• We currently have a number of custom built elearning applications deployed within our program
– I-Help Discussions: An asynchronous web-based
discussion forum
– I-Help Chat: A real-time topic-based chat built around IRC
– E-handin: An electronic hand-in system for assignments
– Learning Content Management System: A delivery tool
for course content (learning objects) and quizzes
associated with the content
• These systems are distributed both in deployment
(machines) and in production (different
development teams)
– How can we model a learner in such a system?
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Massive User Modelling System (MUMS)
• To address these needs we have created a
framework (MUMS) that facilitates the collection
and distribution of learner modelling information
• The central artifact of the framework is the opinion:
– objective data about a user
– relevant from the perspective of who created it
– time-dependant in nature (when was it valid)
• Opinions are not constrained to any particular
ontology or vocabulary
– different producers of modelling information can use
whatever taxonomies and vocabularies they feel are
expressive
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MUMS – 3 Entities
• Opinions are used by three computational entities
– Evidence Producers: observe user interaction with an application and
produce and publish opinions about the user.
– Modellers: are interested in acting on opinions about the user,
usually by reasoning over these to create a user model (e.g. the
tutor!)
– Broker: acts as an intermediary between producers and modellers,
providing routing and quality of service functions for opinions.
• From this, we can derived fourth entity of interest
(adaptor pattern)
– Filter: act as broker, modeller, and producer of opinions. By
registering for and reasoning over opinions from producers, a filter
can create higher level opinions.
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MUMS – Architectural Overview
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MUMS – Benefits of architecture
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Amongst other benefits, this architecture reduces
the coupling between producers and modellers
allows for adding new entities to the system in an
dynamic manner
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New grad students == new data collection/production
needs
Maintains system coherence
New ideas get real usage data immediately!
But with this reduced coupling comes an
important question:
How can we support privacy in a
system designed to be neutral
as to the information it transmits?
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Privacy Agents as Filters
• If we add a filter to the MUMS broker aimed
specifically at supporting privacy, then this filter
needs to understand the user modelling information
it receives
• Intelligent personal agents seem to be a natural
paradigm choice
– Agents could be filled with both the knowledge of what
information should be allowed to pass (ontologically)
– Agents can interact with the learner keeping them aprise
of who is receiving what modelling information
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• But challenges arise
– Learners are not in complete control of their usage
information, institutional control is also important
 Using modelling information for evaluation
 Providing reduced functionality for the learning environment
• The effects of missing modelling data could lead to
chaos
– Currently no method of understanding how information is
being used, just by whom
– Can we further this vision of control with trust networks,
and begin to start asserting how data will be used?
Copyright © 2004.
For more information
Jim Greer
Director, ARIES Laboratory
University of Saskatchewan
Saskatoon, SK, Canada
greer@cs.usask.ca
http://www.cs.usask.ca/research/research_groups/aries/
Copyright © 2004.
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Evaluation - Post Privacy Preferences
Reveal Less
Same
Reveal More
CS
2
3
13
Non-Science
2
4
8
Total
4
7
21
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