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Theme 3: Active and Adaptive Learning ObjectsTheme 3: Active and Adaptive Learning Objects
“Influenced by and Influencing Social Computing”“Influenced by and Influencing Social Computing”
Jim Greer, Gordon McCalla, Ralph Deters, Julita Vassileva
Department of Computer Science
University of Saskatchewan
University of Saskatchewan
IntroductionIntroduction
Why social computing? Our deployed learning environments
have convinced us that there is an increasing social dynamic to be captured
This dynamic has two sides relevant to educational technology research: It’s important. Learners collaborate just-in-
time all of the time, and expect nothing less. Access to email, chat, and instant messaging within a learning environment has changed the ways learners do this online.
We can capture it. Learners are turning increasingly to technology to engage in their learning activities, and we have the option to be in the thick of it all.
Introduction 3
Specific projects:
▪ Jim Greer 4
▪ Julita Vassileva 4
▪ Ralph Deters 7
▪ Gord McCalla 8
Conclusions 2
University of Saskatchewan
Social Computing in E-LearningSocial Computing in E-Learning
We teach/learn in a [usage] data saturated environment
The tools in iHelp capture this attention metadata iHelp Courses, a standards-based research
LCMS iHelp Discussions, an asynchronous forum
system iHelp Chat, a synchronous forum system iHelp Share, online collaborative code
annotation groupware (demo at poster session)
We aim to capture fine grained attention metadata Who reads what? [post/object/chat] How long do they read it?
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Making Sense of DataMaking Sense of Data
Of course, usage data isn’t the only data of interest Content data
We are working with theme 1 (SFU) theme 4 (Waterloo) to dig into this data a bit more Can ontologies help to organize and provide
deductive reasoning over our collected data? Can ontologies provide a bridge between real
usage data and learning standards (e.g. IMS LD)?
How do user inputs (e.g. collaborative tagging) compare to automatic metadata generation?
Can content metadata be leveraged alongside content interaction metadata?
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Awareness and Assistance in iHelpAwareness and Assistance in iHelp
Are my friends around? Who is doing what? Where do I stand? Who is willing and able to help? Is an instructor available? What resources might fit my needs right
now?
Who is at risk? How healthy is the learning
environment? What kinds of interactions are occurring?
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Specific Projects - Jim GreerSpecific Projects - Jim Greer
Christopher Brooks
Basic Approach
Real systems, real learners
Large scale deployments
Collaboration in a safe environment
Building respect for privacy
Enabling and utilizing publicity
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
iHelp ShareiHelp Share
The iHelp project is still ongoing
Collaborative document annotation Programmer help Writing help Augmented by chat and discussion or voice
Why not collaborative editing?
Research opportunities Willingness to collaborate Tutor training
Demo by Stephen Damm, student
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Privacy in e-LearningPrivacy in e-Learning
Virtual learning communities may not be a circle of close friends
How to protect privacy Add pseudonymity
Building trust through reputation But without full identity
Reliable sharing of reputation data How? What about fusing partial reputations? What about transfer of reputation?
Poster by Mohd Anwar, PhD student
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
E-Portfolios to Learner ModelsE-Portfolios to Learner Models
Learner model is a detailed cognitive representation of a learner
Can an e-portfolio initialize a learner model? What information can be automatically
extracted? How can evidence be used to support claims
about cognitive abilities?
The process of “evidencing” Reflection has its benefits
User study
Poster by Zinan Guo, MSc student
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Specific Projects – Julita VassilevaSpecific Projects – Julita Vassileva
Comtella – a community for sharing Participation is the key problem!
Previous (now deployed) approaches: Incentive mechanisms: rewarding participation
through social visibility, status and privileges Successful, but do not necessarily help learning
(students “optimize” their participation to yield the rewards)
New approaches: Making the system immediately useful –
embedding sharing into Personal Information Management (PIM) – in blogs
Exploiting/Fostering interpersonal relationships to generate recommendations of RSS
Bridging communities – in this way even small communities can reach a “critical mass” since the community doesn’t need to provide all the services and users don’t need to start from scratch
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Collaborating through blogsCollaborating through blogs
Sharing? Why? Need to be useful for self first, then to others Sharing by default? Personal info management Need to be convenient, manage access seamlessly
Blogs – personal info space Currently – open for everyone to see (like a
homepage), e.g. MySpace, LifeJournal Managing access rights – very much needed Who sees what? Delegating access rights to groups. Collaborating – allowing others to modify blog
Prototype – a blog system allowing users to manage access rights to their blogs
Special language: user groups, access rights packages (roles), item groups (rooms)
Usability evaluation
See Indratmo’s poster (PhD student)
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Social networks for recommending contentSocial networks for recommending content Current recommender systems:
Content based, Collaborative and Hybrid Collaborative recommender systems use data about past
user actions (rating, buying), correlates it and finds users who have liked similar things in the past recommendations
However, recommendations are faceless “people who in the past have bought similar things like you bought this item”.
Information spreads using social networks Diseases spread also using social networks
Open model of the relationships of influence between users,
show it to users, allow users to add /remove people of influence they wish use these relationships to recommend content applied to recommend RSS
Evaluation: outperforms classic collaborative filtering even in a static database
Applicable also for recommending new items See Andrew Webster’s Poster (MSc student)
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Bridging online communitiesBridging online communities
Currently, online communities are “islands”. Can we enable users to seamlessly jump across
communities, without abandoning their old communities?
Three problems: Identity management across communities Translation of user data across communities Negotiation of policies across communities
Exploring solutions in the Comtella system Mutli-community, multi-node framework Different user roles, rights and priviledges Communities and nodes are autonomous, with own
policies. Decentralized user modelling
See Tariq Muhammad’s poster (MSc student)
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Scalability & Mobility – Ralph DetersScalability & Mobility – Ralph Deters
How to enable scalable solutions? Open, agile, manageable, etc… with great
performance
How to support users of mobile devices? Support the mobile learner, anytime,
anywhere
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Scalability & ManageabilityScalability & Manageability
Delivering/Accessing Learning Objects via Web Services
SOAP is Expensive
How to handle large volumes of requests?
Scheduling of Requests
Dmytro Dyachuk’s Poster
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Scalability & ManageabilityScalability & Manageability
Defining Learning Workflows
Use variety of accessible Learning Objects How to manage instances of workflows? How to ensure SLA? ……
Management of Workflows
Dong Liu’s Poster
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Scalability & ManageabilityScalability & Manageability
Accessing Learning Objects What happens if some LO are not accessible? How to use redundancy? How to ensure more reliable access? P2P?
Integration of P2P into Web Services Self-organizing Dynamic discovery
Weidong Han - Work completed
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Supporting Mobile LearnersSupporting Mobile Learners
Enabling access without stable networks!
Weak connectivity(Low-bandwidth and
intermittent connection )
Strong Connectivity(High-bandwidth
and reliable connection )
Null Connectivity(disconnection )
Laptop
Laptop
Laptop
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Challenges Challenges
Wireless Network
XML
XML
XML
XML
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Supporting Mobile LearnersSupporting Mobile Learners
Using a cache to overcome signal loss!
How to cache What to cache? How to cache? When to cache? Location of cache? …..
Model-Driven Caching
Xin Liu’s Poster
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Specific Projects: Gord McCallaSpecific Projects: Gord McCalla
Basic philosophy fragmented learning environments: just
in time learning, mediated by each individual learner’s various virtual communities
active learner modelling: model only what is needed for a particular pedagogical purpose
ecological approach: each learning object in a learning object repository has attached to it all the information known about each learner who interacted with it and what the interactions were at a fine-grained level; these learner model instances can be mined for interesting pedagogical insight
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Research Paper RecommenderResearch Paper Recommender
Tiffany Tang, Ph.D., expected 2007 capturing pedagogical features of
research papers in order to recommend them to students who are learning about a research area
matching these pedagogical features to models of learners to determine which papers are appropriate for which learners
ties in to ecological approach: can we capture information about learners’ actual interactions with the learning material in order to make better recommendations?
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Data Mining of Learner InteractionsData Mining of Learner Interactions
Wengang Liu, M.Sc., expected 2007 huge amount of interaction data in iHelp
and iHelp courses are there patterns in these data? one approach: bottom-up from data
trying to find pedagogically useful patterns, using data mining and clustering algorithms
current approach: define pedagogically interesting aspects of the learner and try to build metrics to measure these aspects
ties in to ecological approach see poster at this conference
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Building Usable MetadataBuilding Usable Metadata
Scott Bateman, M.Sc., expected 2007 goal is to make the tagging by humans
of learning objects more flexible and more useful
one approach: social tagging by the learners, implemented in OATS system
another approach: use WordNet as a closed ontology from which learners (and teachers) select metadata vocabulary, implemented in CommonFolks system
look for OATS demo, talk on CommonFolks, and poster
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
OATS ScreenOATS Screen
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Purpose-Based Open Learner Purpose-Based Open Learner ModellingModelling Collene Hansen, M.Sc., expected 2007 goal is to open the learner model to the
learner, the teacher, or to other learners when appropriate
for various pedagogical purposes active models of learner(s) can be computed and displayed appropriately
can be very informative to learners and teachers
system built and now being tested in courses at U. of S.
see example, next slide
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
An Example Purpose and VisualizationAn Example Purpose and Visualization
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
Enhancing Social CapitalEnhancing Social Capital
Ben Daniel, Ph.D., expected 2007 goal is to understand what affects social
capital in virtual learning communities and in distributed communities of practice
many empirical studies carried out, seeking variables and studying their affect
modelling of variable interactions with Bayes-Nets
see paper at this conference
Introduction
Specific projects:
▪ Jim Greer
▪ Julita Vassileva
▪ Ralph Deters
▪ Gord McCalla
Conclusions
University of Saskatchewan
The Broader PictureThe Broader Picture
In addition to in-lab projects, we are working with industrial and other partners Parchoma Consulting: Dissemination of the state
of the art in learning object practice. Developed and deployed content for the Canadian Association of Prior Learning Assessment (CAPLA), using iHelp as a basis
Desire2Learn: Initial meetings on technology integration, focusing on issues in and around data mining
Technology Enhanced Learning: Cooperating with university endeavours to realise iHelp systems in a larger scenario, and bringing the concepts of sociability into the online classroom
TR Labs: Working with theme 3 on many of the systems issues that crucially affect performance of e-learning systems
Introduction
Specific projects:
▪ Jim Greer
▪ Ralph Deters
▪ Julita Vassileva
▪ Gord McCalla
Conclusions
University of Saskatchewan
ConclusionsConclusions
The educational technology domain could be a role model for new methods in psychological and social sciences research Learning is necessarily situated in the real world –
small experiments and “controlled studies” have limited impact
E-learning provides environments that are both saturated in data about learner interactions and also about which we can know much about learner purposes and goals: implies we can carry out fine grained studies in the real world
This kind of education research may be a prototype for fine-grained studies of people in various kinds of social situations, not just in learning contexts
Happy to answer any questions!
Introduction
Specific projects:
▪ Julita Vassileva
▪ Ralph Deters
▪ Jim Greer
▪ Gord McCalla
Conclusions