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The Ecological Approach to E-Learning
Gord McCalla
ARIES Laboratory
Department of Computer Science
University of Saskatchewan
Saskatoon, Saskatchewan
My Research Perspectives
• My background
– 37 years in AI research (I started when I was 4!)
– first 10 years in natural language dialogue and knowledge
– since then mostly artificial intelligence in education (AIED) and
user modelling (UM)
• Current research areas
user modelling
multi-agent systems
recommender systems
some natural language pragmatics stuff
virtual learning communities
Talk Outline
• AIED as a crucible for research
• Overview of my research projects
• Finding coherence in my research projects
• the ecological approach
• Four ecological projects
active modelling of learners
research paper recommender
LORNET Theme 3
• What does it all mean for AI and AIED?
Artificial Intelligence in Education
• My research is situated in the area of artificial intelligence in education
(AIED): advanced systems to support human learning
– is an applied area of AI (and education)
– draws from a wide variety of disciplines: education, psychology, sociology,
anthropology, computer science (AI): need advanced technology and
advanced social science
– emphasizes building working systems to be used with real users (learners)
– usually puts the learner at the centre: learner modelling
– is not concerned with formal issues of soundness, completeness and
consistency, but with the practical issues of
resource constraints
AIED is a Crucible for AI Research
• AIED is AI-complete, perhaps human knowledge-complete
• Is it tractable?
the domain is naturally limited
the focus is on information not the physical world
the learner is naturally constrained
the learner is naturally forgiving
there are many humans already involved in supporting learners,
including teachers and the learners themselves
– there is much research to draw on from a wide variety of
My Current Research: Apparent Chaos?
• My current research projects
– LORNET (Learning Object Repository Network):
• NSERC network of centres of excellence: major national project (Simon Fraser
U, TelUQ, Montreal, Saskatchewan, Waterloo, Ottawa)
• Theme 3: active and adaptive learning objects (with Greer, Vassileva, Deters,
– research paper recommender system (Tang)
– capturing user goals in purpose hierarchies for “just in time” active user
modelling (Niu, with Vassileva)
– open learner modelling in an active context (Hansen)
– new agent negotiation paradigms (non-monotonic offers, strategic delay,
ignorance-based counter argument) (Winoto, with Vassileva)
– impeding spread of delusion in agent models (Olorunleke)
– enhancing social capital in virtual learning communities (Daniel, with
– data mining patterns of learner interaction with an e-learning system (Liu)
– mapping “folksonomies” of meta-tags on learning objects (Bateman)
• Is there some whole emerging from these parts??
Bringing Order out of Chaos!
• A number of forces are driving systems that support
learning: there is increasing fragmentation of
– culture
• each learner embedded in cyberspace, has local perspectives
connecting to huge global world of information and other people
– learning
• knowledge flows through virtual communities to/from the learner, and
transforms en route
• much learning happens “just in time”, when learner needs to know
– teaching
• teaching becomes support for learning, in context of learner’s goals
– technology
• boundaries of software blur: importing/exporting computation
• behaviour of such software systems will be emergent, like an
ecosystem, fundamentally unpredictable
Bringing Order out of Chaos!
• Need to build AIED systems that are consistent with the
fragmented perspective
– software architecture
• multi-agent
– knowledge base
• dynamic, oriented around change not consistency
– learner modelling
just in time
understand learner’s purpose
track changes
model communities, not just individuals
– pedagogical strategy
• nuanced, supportive, context sensitive
• take advantage of communities
– research sources
• look broadly in computer science and to the social sciences and
The Ecological Approach
• I have been working on an AIED architecture consistent
with the fragmented perspective: the ecological approach
• It has the following characteristics:
– 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 the ecological architecture
– the AIED system
• learners are represented in the learning object repository by personal agents
• each personal agent advises its learner on how best to interact with the
learning object repository, essentially the custodian of pedagogical advice;
many types of advice
– recommend a learning object or a sequence of objects
– provide diagnostic advice to the learner
– find a helper for the learner, a human tutor or peer
– help the learner find a learning community
• each personal agent has on board a model of their learner and possibly models
of other learners
• as a learner interacts with a learning object, the personal agent is always in the
loop, advising the learner according to the learner’s goals and the agent’s
pedagogical purposes, and actively updating its model(s)
• after a learner has interacted with a learning object, a copy of the learner’s
model, as kept by the personal agent, is attached to the learning object
• over time, learning objects will be adorned with learner models of many
learners (and even, possibly, the same learner many times)
• these learner model instances can be mined for useful information
learning/cognitive style
previous learning objects
current goal(s)
trace of learner’s interactions
learner’s view of content
learner’s evaluation of object
The Ecological Approach
• Two key technologies
– active modelling
• each personal agent tries to keep track of the learner’s current purpose(s)
• it then mediates its interactions with the learner in ways appropriate to the
learner’s purpose(s) and its own pedagogical goals
– it only uses (or computes) information about the learner that it actually
– the learner model is actually just a residue of many such purpose-based
active computations
– context is thus central: the learner, other humans, resources, purposes
and goals
– mining learner model instances
• to find out which learning objects are relevant to a learner for their purpose(s):
learning object recommender system
• to find a sequence of such objects: 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
• to find out what happened to a learner or learners: empirical evaluation
An Example
The Ecological Approach
• The approach is ecological
– the environment is populated by many agents and learning objects
(possibly changing over time)
– the agents and objects constantly accumulate more and more
– 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
– data mining and clustering, especially to actively compute patterns
connecting particular types of learner to particular types of
– collaborative filtering and case-based reasoning, which essentially
underlie much of the active decision making
Current Ecological Research Projects
• I-Help: the font
– Greer, McCalla, Vassileva, Deters, Cooke, Kettel, Bull,
Collins, Meagher, graduate and summer students
• Active learner modelling: the paradigm
– Vassileva, McCalla, Greer, graduate students
• Research paper recommender: the prototype
– Tiffany Tang, McCalla (supervisor)
• LORNET: the critical mass
– McCalla, Greer, Vassileva, Deters, Cooke, Brooks, Winter,
graduate students
I-Help: Supporting Peer Help
• Two components
– I-Help Pub: open peer forum
– I-Help 1-on-1: find a ready, willing, able peer
• Agent-based
– personal agents representing learners and applications
• Fragmented learner modelling
– each agent keeps models of other agents
• Testing
– wide-scale deployment of Pub (1000’s of users)
– pilot studies of 1-on-1
• Current and future directions
– mining Pub to supply information for 1-on-1
– full integration and effective performance
An I-Help Screen
Active Learner Modelling
• learner models aren’t stored, but are computed in
• main context elements: learners, purposes
• current investigations:
– purpose hierarchies in e-commerce domain: purpose is to
match a user to a stock broker agent (Niu)
• can the domain be covered?
• can you get purpose re-use?
– open active modelling: in domain with many purposes:
supporting learners and teachers (Hansen)
• how and when do you open a learner model that doesn’t
Research Paper Recommender
• Tiffany Tang’s Ph.D. thesis
• recommending papers to graduate students preparing
for research in a domain (eg. data mining)
• learner models of readers attached to papers
• recommendations made by clustering learners
according to these models and predicting usefulness of
papers for the student based on the cluster they map to
• most of the research has been investigating what
pedagogical features should underlie the
LORNET Project
• Five year NSERC-sponsored research network
investigating learning object repositories:
theme 1: interoperability (SFU)
theme 2: aggregation (TelUQ)
theme 3: active and adaptive learning objects (U. Sask.)
theme 4: learning object mining (U. Waterloo)
theme 5: multi-media and learning objects (Ottawa U.)
theme 6: integrative theme: telelearning operations system
(TelUQ, and the rest)
LORNET - Theme 3
• explore ecological approach to capturing and using information
about learners (McCalla)
• MUMS user modelling middleware (Brooks, Winter)
• instructional planning and recommending through agent
negotiation (Vassileva)
– personal agents and agents representing learning objects
granularity of learning and learning objects (Greer)
privacy (Greer)
learning object (agent) reliability and scalability (Deters)
design, construction, deployment, and evaluation of application
– in partnership with industrial sponsors (TRLabs, Parchoma Ltd.)
– two entirely on-line courses with 1000’s of learning objects: CS
service course; CS readiness course
– module of first year CS service course fully “wired” for ecological
data collection: will be mined (Liu) and issues in meta-tagging will
be explored (Bateman)
The Appeal of the Ecological Vision
• learning objects are activated: they are not passive,
but take on responsibilities for their use in support of
• learners are “in the loop”: personal agents allow
learners to be part of the educational environment
• focus is on end use: essentially learning objects are
tagged by models of the learners who use them, not
by context-independent content tags from a predefined ontology
• approach is ecological: as end use experience
accumulates, there can be an ever more refined
understanding of what works for whom
The Appeal of the Ecological Vision
• decision making is contextual: information is actively
interpreted in context and as needed for more
appropriate reactions
• approach is extensible and adaptable: the agentbased approach allows new learning objects and
learners to be added, old ones to be deleted
• approach is modular: agent approach localizes
decision making and improves robustness
• approach supports diversity: learners, applications,
and learning objects can be integrated into one
system, unified by the agent metaphor
Is the Ecological Approach Tractable?
• computational issues
– how much can be done actively
– space-time trade-offs
– 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
– advantages of e-learning application
• environment can be constrained
• learner can be constrained
• feedback from learner is natural and serves a pedagogical purpose
Déjà vu?
• Doesn’t this seem somehow familiar?
active modelling: procedural approach
fragmented technology: frames/actors
associative links among learning objects: semantic networks
looking outside of AI for other paradigms
building big systems and seeing if anybody salutes!
• These were big AI issues in the 1970’s
– good old fashioned AI (GOFAI)
– what goes around, comes around: the cycle of research
• Isn’t it somehow different?
– data-centric: machine learning was not central then
– emphasis on end-use context: context was usually ignored then
– needs powerful computational engine: not available then
• What works for AIED may work for many AI application
– computer games, natural language understanding, AI-based ecommerce, even computer vision
• AIED forces deep issues to be grappled with
– much current AI is exploration of algorithm space or theoretical
issues without the “reality” check provided by applications such as
– precision in a vacuum is indeed a vice!
• AIED is thus a crucible for AI research
• Can AIED once again be a mainstream area of AI, feeding
ideas into AI as well as vice versa?
Questions, Comments, Interactions
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
– private sector support: TRLabs, Parchoma Consulting Ltd.
Some References
– 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.
– J. Vassileva, G.I. McCalla, and J.E. Greer, “Multi-Agent Multi-User Modelling
in I-Help”. 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.
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