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Learner Models as Metadata
to Support E-Learning:
The Ecological Approach
Gord McCalla
ARIES Laboratory
Department of Computer Science
University of Saskatchewan
Saskatoon, Saskatchewan
CANADA
Take Home Message
• A main source of information for a system is information
about user interactions with the system
– often don’t need to have any other explicit metadata
– don’t have to ask the user for their feedback
• In educational environments there is often already a
synthesized model of these interactions for each user:
the learner model
– so, why not use this learner model as a source of insight into a
learner so the educational environment can be adapted to the
learner?
attach learner models to educational material
and mine these for patterns that can inform
the educational environment in achieving
various pedagogical purposes
Talk Outline
• Motivation: learner models as metadata
• The ecological approach, formalizing the idea of learner models as
metadata
• Some ARIES research into issues important to the ecological approach
– the data source: iHelp
– active learner modelling: “just in time” purpose-based learner modelling
• finding patterns in data
– metrics
– data mining
• purpose-based modelling
– hierarchies of purposes
– purpose-based open modelling
• MUMS middleware for customizing interpretation of interaction data
– agents
• alternative agent negotiation paradigms
• agent modelling
• Two other SWEL projects
• social tagging: CommonFolks, OATS
• Implications of the ecological approach
Motivation
Learner Models as Metadata
Trends and Challenges for the Semantic Web
• Ongoing huge growth in
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amount of information available electronically
number of users wanting to access this information
range of purposes these users have for wanting to access this information
social connections among these users
tools for accessing and massaging this information and for connecting to
the users
• This has major challenges for the semantic web
– how can metadata be attached to all of this information?
– how can you know what kinds of metadata to attach, given the variety of
purposes to which any resource can be put?
– how can you deal with change: new information, changing old information,
new users, etc.?
G.I. McCalla, “The Fragmentation of Culture, Learning, Teaching and
Technology: Implications for the Artificial Intelligence in Education
Research Agenda in 2010”. Special Millennium Issue on AIED in
2010, Int. J. of Artificial Intelligence in Education, 11, 2000, 177-196.
The Semantic Web and E-Learning
• E-Learning is a more tractable place than the open web to
explore these semantic web issues
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the learning environment is (more) closed
the material to be learned is (more) stable
the users are (better) known
the purposes of these users are (more) explicit
there are humans (learners, teachers, tutors, etc.) embedded in
the system who can take on various roles
• to support the system
• to support each other
– and yet, a learning environment is a fully representative microcosm
of the more open web so lessons learned are likely to be
generalizable beyond e-learning
SWEL is where it’s at!
A Generalized E-Learning Architecture:
Learning Object Repository
• We can envisage a generalized e-learning system:
a learning object repository
– the repository consists of a wide variety of learning objects, including
articles, web pages, discussion forums, simulations, quizzes, intelligent
tutoring systems, etc.
– learners, teachers, tutors (collectively users) can also be considered as
“embedded” in the repository
– learners spend much of their learning effort interacting with these learning
objects and with each other
– these users can form virtual communities
– inside the repository, users are tracked in detail
– the repository is closed, but can “project” much more widely, as users are,
of course, free to look outwards into the vast expanse of the web
– the environment has many tools to support its users
• Many types of e-learning system can be mapped into this learning
object repository architecture
An Example Learning Object Repository System
• Tiffany Tang’s Ph.D. project
– the context: graduate students trying to learn a new area of research
by reading papers and articles about the area
– the goal: for a given learner, recommend paper(s) appropriate to that
learner at that stage in their understanding of the domain
– the learning object repository:
• research papers are the learning objects, kept in a repository
• attached to the learning objects can be
– attributes of the learners (learner models)
– characteristics of the papers (tagging)
– history of the interactions of the learners with the repository
(interaction data)
– the research goal: to examine how graduate students choose and
evaluate research papers to discover relevant pedagogical features
see Tang and McCalla papers in E-Learning J., 2005; AIED conference,
2005; book chapter in Romero and Ventura’s collection “Data Mining in
E-Learning”, WIT Press, 2006; IEEE Internet Computing Journal special
issue on e-learning, 2009
How Can Annotating with Learner Models be Used
to Achieve Various Pedagogical Purposes?
• One “pedagogical” purpose: recommend a learning object
for a learner
– assume: there is a learner model for each learner with various
characteristics: personal, affective, learning style, etc (can build up
over time, like an e-portfolio)
– assume: every time a learner has interacted with a learning object
a copy of the model is attached to the learning object along with a
record of the interaction between the learner and the learning
object: a learner model instance
– then: an appropriate learning object for a learner might be one that
other learners (with similar characteristics to the learner who have
interacted in similar ways with similar learning objects) have found
to be useful: collaborative filtering
CHARACTERISTICS
personal
affective
learning/cognitive style
previous learning objects
current goal(s)
EPISODIC
trace of learner’s interactions
learner’s view of content
learner’s evaluation of object
outcomes
Learner
Model
Instance
An Example
x
x
x
x
x
?
x
x
?
Other Pedagogical Purposes
• There are many other pedagogical purposes that could
employ similar methodologies
– to find a sequence of learning objects of relevance to a learner:
instructional planning
– to find out which learning objects are useful, not useful, or no
longer useful: intelligent garbage collection
– to find peers with appropriate characteristics: help finding
– to find groups of learners with appropriate shared attributes:
building learning communities
– to find out what happened to a learner or learners interacting with a
learning object repository: empirical evaluation
– to attach relevant pedagogical information to learning objects
based on actual end use experience: metatagging
– ………
The Ecological Approach
Formalizing the Idea of Learner Models as Metadata
The Ecological Approach
• How to support learning and users in a learning object
repository?
the ecological approach
• An ecological architecture has the following characteristics
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the learning environment: learning object repository
the computing environment: multi-agent system
user and object modelling: active agent modelling
achievement of user and system purposes: mining agent models
G. I. McCalla, “The Ecological Approach to the Design of E-Learning
Environments: Purpose-based Capture and Use of Information about
Learners”. Journal of Interactive Media in Education, Special Issue on the
Educational Semantic Web (eds. T. Anderson and D. Whitelock), May
2004. http://www-jime.open.ac.uk/2004/1
The Ecological Approach
• Characteristics of an ecological architecture
– the learning environment
• all learning materials are created as learning objects
• learning objects can range from relatively inert text objects through
fully interactive immersion environments
• learning objects may be at various grain sizes, with one learning
object potentially breaking down into subsidiary learning objects
• the learning objects are in a learning object repository
• new learning objects can be incorporated into, and old objects retired
from, the repository
• the learning objects can have many associative links to each other
and to the outside world
• learners have final control over which learning objects they select and
how they interact with them
The Ecological Approach
• Characteristics of an ecological architecture
– the computing environment
• learning objects are represented by agents
• users (learners, tutors, and teachers) are represented by personal
agents
• various goals in the learning object repository are carried out by these
agents
– learner agent finds a learning object(s) for a learner
– learner agent finds a helper for a learner, a tutor or peer
– learner agent finds a study group for a learner
– teacher agent evaluates the effectiveness of learning object(s)
– learning object agent finding related learning object(s)
• achieving these goals often requires agent negotiation
– learner agent negotiating with learning object agent about
whether a learning object meets the learner’s pedagogical goals
– two learner agents negotiating whether one learner can help
another learner overcome a learning impasse
– one learning object agent trying to discover if another learning
object agent contains pre-requisite knowledge
The Ecological Approach
• Characteristics of an ecological architecture
– active agent modelling
• each agent keeps its own models of users and other agents
• these models are continuously updated as the agents interact with
each other and with users: active modelling
– each agent keeps track of its current goal and accesses models
of other agents/people implicated in helping it to achieve this goal
– in achieving the goal, new modelling information can be
computed “just-in-time” as needed and as resources allow
– in achieving the goal, interactions with other agents and with
users can also be tracked
– the models are thus continuously updated with fine-grained
information, but this information is contextualized by the goals
being undertaken, the agents and people involved, and the
resources available
– an agent’s overall model of another agent or person is thus a
compendium of many such purpose-based active computations
The Ecological Approach
• Characteristics of an ecological architecture
– mining the models
• the models do not have to be synthesized and summarized into one
all encompassing model
• instead can keep the raw data captured during interaction and mine it
for interesting patterns or patterns useful for achieving various
pedagogical purposes, for example
– to look for changes in a learner’s knowledge over time
– to abstract general characteristics of learners
– to find sub-classes of stereotypical learner behaviour
– to see changes over time in the way a learning object is used by
learners
– to discover the effectiveness of a learning object for different
learners
The Ecological Approach
• Why is this approach “ecological”
– the environment is populated by many agents and learning objects
(possibly changing over time)
– the agents and objects constantly accumulate more and more
information
– there is natural selection as to which objects are useful: could
“prune” useless objects
– there are ecological niches based on purposes: certain agents and
learning objects are useful for a given purpose, others aren’t
– the whole environment evolves and changes naturally through
interaction among the agents and on-going attachment of learner
models to learning objects
The Ecological Approach
• The ecological approach impacts many computational
issues in AI and other areas of CS
– various traditional AIED topics, especially learner modelling and
instructional planning
– various application level agent topics, especially agent negotiation
and agent modelling
– various system level agent topics, especially scalability and
adaptivity
– data mining and clustering, especially to actively compute patterns
connecting particular types of learner to particular types of
outcomes
– collaborative filtering and case-based reasoning, a frequently used
technique in reasoning about user activities
– semantic web, especially alternative strategies for tagging based
on patterns found in end-use data
Current Ecological Projects
ARIES Laboratory Investigations into Issues of
Relevance to the Ecological Approach
ARIES Projects Impacting the Ecological Approach
• The data source
– iHelp
• Active learner modelling: “just in time” computation of
important learner attributes for various pedagogical
purposes
– finding patterns in data
• metrics
• data mining
– purpose-based modelling
• hierarchies of purposes
• purpose-based open modelling
– MUMS middleware
• Agents
– alternative agent negotiation paradigms
– agent modelling
Collecting Data: iHelp
• iHelp aims to support peer-peer interaction in courses
– Greer, McCalla, Vassileva, Deters, Cooke, Hansen, Brooks, Winter, Kettel,
Bull, Collins, Meagher, students, technical staff, over 10 years
• ITS-98 paper has most general architecture description; many other papers
• iHelp has several components
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iHelp courses: on-line courses embedded in iHelp
iHelp discussions: open peer forum
iHelp chat: real time on-line chat
many tools and capabilities: search, preferences, code sharing, …
• Deployed with many computer science courses
– thousands of users over many years; two complete on-line courses
– keystroke level data collection; many experiments using the large and
growing interaction database
• Future directions
– take further advantage of huge end-use database to investigate issues in
learner modelling, personalization, visualization
– privacy tools
– re-institute the 1-on-1 expertise location tool (from earlier versions)
– possibly look at re-instituting personal agents (from earlier versions)
iHelp Courses
iHelp Discussions
Active Learner Modelling
• Exploring active learner modelling
– idea of active modelling is that the learner model is computed as
needed from system-user interaction data
• based on Self’s observation: “don’t diagnose what you can’t treat”
• model as a “verb” not a “noun”
– research agenda is then to explore how to do relevant just-in-time
computations from interaction data
• one wing: finding patterns in learner interaction data
• another wing: working out types of computations that might be carried
out for various pedagogical purposes, i.e. creating purpose-based
programming clichés
• third wing: providing tools to support pedagogical engineer in
customizing processes to interpret interaction data
J. Vassileva, G.I. McCalla, and J.E. Greer, “Multi-Agent Multi-User Modelling in IHelp”. User Modeling and User-Adapted Interaction J., Special Issue on User
Modelling and Intelligent Agents (E. André and A. Paiva, eds.), 13 (1), 2003, 1-31.
Finding Patterns in Data:
E-Learning Metrics and Measurements
• Generation of metrics and measurements from learner data for
automatic learner modelling
– Liu, McCalla (see Brooks, Liu, Hansen, McCalla, Greer in AGILeViz
workshop at AIED-07)
– idea is to generate metrics and measurements that may be pedagogically
useful based on an analysis of raw student interaction data
– basic approach: raw data is pre-processed into “facts”, then these facts
are mined for patterns (using several clustering algorithms), the patterns
put together into measurements (such as page dwell time, number of chat
messages, etc.), and the measurements finally coalesced into high level
pedagogical metrics (such as activity level, social ability, learning style,
knowledge level, etc.)
– metrics and measurements can then be applied to new data, often in real
time
– two experiments, both inconclusive (due mostly to small numbers)
• system-generated metrics compared to metrics generated by human experts
• system-generated metrics compared to alternative questionnaire-based metrics
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QuickTime™ and a
TIFF (LZW) decompressor
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QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Finding Patterns in Data:
Mining iHelp Data
• Data from on-line and in-class versions of introductory
computer science courses mined to see if there is any
difference between them
– Peckham: started as graduate course project
– use of contrast-set association rule mining, particularly useful for
comparing groups
– compared 7 sections of introductory CS course Cmpt. 100, 2 online and 5 in-class, over the 3 years 2005-2007, looking for
differences between on-line and in-class versions
– some marginally interesting patterns, but we are now looking for
more pedagogically useful and consistent patterns
• Long term big question: how much can we actively
compute the learner model from log-file data of learner
interactions?
Finding and Using Purpose-Based Program Clichés
• Another track of research into active learner modelling is
finding and using purpose-based program clichés that
actually carry out the learner modelling computations:
“model as verb”
– Niu, Vassileva, McCalla (Computational Intelligence J., 2003):
hierarchies of purpose-based program clichés to compute user
modelling information for an agent-based portfolio management
system
• experiments with a prototype system in a simulated context were
promising
• not clear if the methodologies can be extended to messy real world
– Hansen, McCalla (open learner modelling workshop at AIED
conference, 2003; also see Brooks, Liu, Hansen, McCalla, Greer in
AGILeViz workshop at AIED-07): purpose-based open learner
modelling
• computing learner models for learners/teachers in Cmpt. 100 course
Hierarchies of Purpose-Based Program Clichés
The multi-agent portfolio management system
Personal Agent2
Personal Agent1
KB
KB
Personal Agent3
Expert Agent1
Expert Agent2
KB
KB
Expert Agent4
Expert Agent3
Personal Agent6
Personal Agent4
Personal Agent5
User Interface
Investor
Hierarchies of Purpose-Based Program Clichés
Purpose - X
To select an agent to match the
needs of a person
Is a
Purpose -1
To select an expert
agent f or an investor
Purpose -2
To select a helper f or
a student in I -Help
ΙΙ
Is a
Purpose -a
To find an expert
agent f or a
risk - seeking
investor
Purpose -b
To find an expert
agent f or a
risk -reverse
investor
ΙΙ
ΙΙ
Hierarchies of Purpose-Based Program Clichés
Purpose -1
Finding an ex pert agent
Part of
Purpose1 -1
Matching user type
and advertisements
Purpose1 -3
Checking trust
in expert agents by
similar investors
Purpose1 -2
Checking most used
expert agent
Purpose1 -4
Do subPurpose1 -3
by taking into
account user types
Part of
Purpose
Purpose
1-1a
1-1b
To get
To get
user
ad.
type
Purpose
1-1c
To
compute
R1
Purpose
1-1d
To sort
EA
based
on R 1
Purpose
1-2a
To pick
k
similar
investors
Purpose
1-2b
To check
which
EA each
PA uses
Purpose1 -2a-1
To ask PA about its
investorΥs user type
Purpose
1-2c
To
compute
R2
Purpose
1-2d
To sort
EA
based
on R 2
Purpose
1-3b
To check
PA about
trust
in EA
Purpose
1-3c
To
compute
R3
Purpose
1-3d
To sort
EA
based
on R3
Purpose
1-4d
To
compute
R4
Purpose
1-4e
To sort
EA
based
on R4
Purpose-Based Open Learner Modelling
• Purpose-based open learner modelling
– Hansen’s M.Sc. project
– learners, teachers, tutors have a need to find out information about
themselves, for reflection, evaluation, motivation, etc.
– provide a tool that allows such end users to generate summaries
that suit their purposes: key is that the information is generated as
needed, actively, and not kept in a learner model
– prototype has been implemented and tested with students and
teachers in several classes
Hansen, McCalla, Open Learner Modelling workshop at AIED
conference, 2003
Teacher Query
Answer to Teacher Query
Peer Comparison
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TIFF (LZW) decompressor
are needed to see this picture.
MUMS - User Modelling Middleware
• MUMS - Massive User Modelling System
– Brooks, Winter, Greer, McCalla (ITS-04 conference)
– idea is that the e-learning system builder can customize processes
to interpret raw data about learner interaction
– naturally de-couple data from its use
• 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
– broker: acts as an intermediary between producers and modellers,
providing routing and quality of service functions for opinions
• From this, we can derive fourth entity of interest (adapter
design pattern)
– filters: act as broker, modeller, and producer of opinions. By registering
for and reasoning over opinions from producers, a filter can create higher
level opinions
MUMS – Architectural Overview
4. Store opinion
5. Route opinions to
interested modellers
and filters
3. Publish opinion to broker
1. Observe user
interaction
Broker
9. Act!
Modeller1
8. Reason over opinions
Evidence
Producers
5. Route opinions to
interested modellers
and filters
8. Reason over opinions
2. Form opinion
about user
9. Act!
Modeller2
Filters
6. Reason over opinions
forming higher level statements
7. Route higher level
Opinions to interested
modellers and filters
Agents
• Agent interaction in the ecological approach provides
interesting challenges to agents research
– alternative agent negotiation protocols: Winoto, Vassileva, McCalla
• agents in e-learning systems often don’t bargain monotonically or
strictly rationally, and must negotiate a multi-dimensional pedagogical
space
• non-monotonic offers: agents don’t always monotonically
reduce/increase their offers: AAMAS-2004; JAAMAS-2005
• strategic delay, strategic ignorance: AAMAS-2007
– agents modelling other agents: Olorunleke, McCalla
• in order to negotiate and communicate with one another agents need
to model each other (actively?)
• controlling spread of delusion in agent societies: AAMAS-2003
• agent modelling in real time (RoboCup domain)
Two Other SWEL Projects
Research into Other Kinds of Metadata for
Learning Objects
OATS and CommonFolks
• It may be possible to annotate educational material in
pedagogically relevant ways with explicit metadata that is
easily captured from learners and teachers
– CommonFolks: combining social and semantic tagging of learning
objects: Bateman, Farzan, Brusilovsky, McCalla, SW-EL @AH
2006
– OATS: building a tool that allows learners to highlight and annotate
text-based learning objects, discuss these highlights with one
another, and add semantic and social tags: Bateman, Farzan,
Brusilovsky, McCalla, I2LOR 2006
CommonFolks: Combining Social and
Semantic Tagging
• Semantic annotation is difficult,
pedagogically focused metadata
is important for (re) using learning
objects , but authors, librarians or
instructors will readily apply it
• CommonFolks investigates a
process that is similar to simple
social tagging (applying and
sharing simple keywords), but to
create semantic tags
• CommonFolks tags contain a
predicate (property) and a value
from an evolving dictionary, but
has no predefined vocabulary like
the LOM
• Perhaps the process still requires
too much effort of learners and/or
teachers?
OATS: Open Annotation and Tagging System
• How can we encourage students to create and annotate
content?
• OATS applies annotations in a simple way, using
highlights, created by clicking-and-dragging over text in a
webpage
OATS: Open Annotation and Tagging System
• Users can organize and annotate a highlight by clicking
on it, revealing a popup interface
• Tags and notes can be applied and are shared, so
learners can see what others think about a particular part
of a learning object
OATS: Open Annotation and Tagging System
• Highlights are also shared. Learners can see what their
peers have determined is the most important part of the
text (emerges from highlights created). The higher the
pink highlight the more all other students have
highlighted that part of the text
• A class study showed that learners liked and were
excited about creating annotations using OATS,
especially being able to see what others thought was
important
• But as a self-directed organizational scheme, users
would need to be able to widely use the system over
many courses and multiple years
Implications
Is the Ecological Approach Actually
Tractable? If So, What Does it Imply?
Is the Ecological Approach Tractable?
• Computational issues
– how much can be done actively?
– space-time trade-offs: how much to save, how much to pre-compute?
– can purposes and learner models constrain the mining, clustering, and
filtering algorithms?
– can purposes cover a domain and be re-used in other domains?
– can learner models be standardized and shared?
• Social issues
– what kinds of pedagogy can be supported?
– can social capital be enhanced? (Daniel, Schwier, McCalla, AIED
conference, 2005)
– advantages of e-learning application
• environment can be constrained
• learner can be constrained
• feedback from learner is natural and serves a pedagogical purpose
Broader Implications?
• Are there lessons of the ecological approach beyond AIED?
• Some wild speculation!
– the semantic web
• is it only necessary to tag objects (web pages, etc.) with user trace data?
– impossibility of tagging everything
– user model instances and interaction data can be naturally gathered
and then mined according to purpose (OATS community tagging and
visualization tool may be a step in this direction)
– focus is on end use and users, not content; maybe it should be the
pragmatic web?
• maybe there needs to be effort spent on determining how to negotiate
differences between ontologies?
– impossibility of universal agreement upon standard tag sets,
ontologies
– agents representing each ontology could perhaps negotiate content
and social differences, much as internet service protocols negotiate
differences in technical environments
Broader Implications?
• Wild speculation
– user modelling
• how much user modelling can be done actively?
• user model maintenance through re-computation, not belief revision
• life long user modelling
– a person’s hard disk contains a huge amount of information about
them over the long term
– what kinds of patterns can be found by mining this for what kinds
of purposes?
– what about information about the person kept in other hard disks?
– life long user modelling would have to be largely active
– references: UMAP 2009 workshop organized by Judy Kay, Bob
Kummerfeld; workshop hosted October 2008 at Institute for
Creative Technologies by Chad Lane
Broader Implications?
• Wild speculation
– artificial intelligence
• AIED as a challenge problem for AI that is not concerned with formal
issues of soundness, completeness and consistency, but with the
practical issues of
– robustness
– effectiveness
– context
– change
– resource constraints
• ecological approach as a challenge paradigm for AI applications
– integrating many AIED, AI, computer science, and social science
issues
– applicable to many e-learning paradigms
– can it be applied in less constrained domains than AIED?
Broader Implications?
• Wildest speculation
– social science
• a revolution in social science research methodology - the tyranny of the
control group experiment may be overcome?
– the increasing bandwidth of human interaction with and through
technology means that a large amount of hard data can now be
collected in natural social settings (mediated through ICT) and can
provide evidence for cognitive and social theories
– social scientists, like astronomers a few centuries ago, can use
new tools and techniques to both collect and make sense of this
data
• data mining and clustering methodologies
• statistical techniques
• representation techniques
• HCI techniques
• education is perhaps the social science best situated to explore this
revolution - the high interaction bandwidth and natural constraints of
educational domains makes them ideal for pioneering experiments
• which brings us full circle to where we started, so lets end!
Questions, Comments, Interactions
?
Contact me at
[email protected]
Acknowledgements
–
–
–
–
my graduate students past and present
my colleagues in the ARIES Laboratory
our research associates past and present
funding from the Natural Sciences and Engineering Research
Council of Canada
• discovery grant
• LORNET networks grant
– the Government of Saskatchewan through the TEL program
– private sector support: TRLabs, Desire-to-Learn, Parchoma
Consulting Ltd
Some References
– T.Y. Tang and G.I. McCalla, “Active, Context-Dependent, Data-Centered
Techniques for E-Learning: A Case Study of a Research Paper Recommender
System”, in Data Mining in E-Learning (eds. C. Romero and S. Ventura), WIT
Press, Southampton, UK, 2006, pp. 97-116.
– C. Brooks and G.I. McCalla, “Towards Flexible Learning Object Metadata”, Int. J.
of Continuing Engineering Education and Life Long Learning, Special Issue on the
Application of Semantic Web Technologies in E-Learning (D. Dicheva, ed.), 16,
1/2, 2006, 50-63.
– G. I. McCalla, “The Ecological Approach to the Design of E-Learning
Environments: Purpose-based Capture and Use of Information about Learners”.
Journal of Interactive Media in Education, Special Issue on the Educational
Semantic Web (eds. T. Anderson and D. Whitelock), May 2004. http://wwwjime.open.ac.uk/2004/1
– J. Vassileva, G.I. McCalla, and J.E. Greer, “Multi-Agent Multi-User Modelling in IHelp”. User Modeling and User-Adapted Interaction J., Special Issue on User
Modelling and Intelligent Agents (E. André and A. Paiva, eds.), 13 (1), 2003, 1-31.
– G.I. McCalla, “The Fragmentation of Culture, Learning, Teaching and Technology:
Implications for the Artificial Intelligence in Education Research Agenda in 2010”.
Special Millennium Issue on AIED in 2010, Int. J. of Artificial Intelligence in
Education, 11, 2000, 177-196.
•Learner Models as Metadata to Support E-Learning: The Ecological Approach
•Invited Talk at SWEL Workshop @ AIED 2009
•Gord McCalla
•ARIES Laboratory
•Department of Computer Science
•University of Saskatchewan
•Saskatoon, Saskatchewan
•CANADA
•Abstract
•In this talk I will present an e-learning framework called the ecological approach that is based on the idea that learner models are a
rich source of metadata that can be used to inform various activities of an environment that supports human learning. Learner models
can provide insight into learning material that might be appropriate for a learner, guidance about other learners who might be able to
help a learner who is at an impasse, useful information to create and support learning communities, or even data that can be used for
empirical analysis of the effectiveness of an e-learning environment. I will discuss the characteristics and implications of the
ecological approach, for e-learning and beyond. I will also mention some of the research currently underway in the ARIES laboratory
that may lead to more ecological e-learning systems.
•Short Biography
•Gord McCalla is a Professor in the Department of Computer Science at the University of Saskatchewan in Saskatoon, Canada. His
research interests are in applied artificial intelligence, focussed particularly on user modelling and artificial intelligence in education
(AIED). Working with colleagues and students in the ARIES Laboratory at the U. of S., he has explored many issues, including
granularity in learning and reasoning, educational diagnosis, learner modelling, tutorial dialogue, instructional planning, peer help,
and learning object repositories. Recently, he has begun to look into the implications of “fragmented learning systems”, systems that
are designed to support learners in diverse virtual learning communities (social fragmentation) and systems that are themselves
composed of many software agents (technological fragmentation). This has led to an AIED architecture called the “ecological
approach”, currently being explored in a number of research projects in the ARIES Laboratory.