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The Ecological Approach to E-Learning. Gord McCalla ARIES Laboratory Department of Computer Science University of Saskatchewan Saskatoon, Saskatchewan CANADA. My Research Perspectives. My background 37 years in AI research (I started when I was 4!)

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The Ecological Approach to E-Learning

Gord McCalla

ARIES Laboratory

Department of Computer Science

University of Saskatchewan

Saskatoon, Saskatchewan


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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 representation

    • since then mostly artificial intelligence in education (AIED) and user modelling (UM)

  • Current research areas

    • AIED

    • user modelling

    • multi-agent systems

    • recommender systems

    • some natural language pragmatics stuff

    • virtual learning communities

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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

    • I-Help

    • active modelling of learners

    • research paper recommender

    • LORNET Theme 3

  • What does it all mean for AI and AIED?

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Artificial Intelligence in Education

  • My research is situated in the area of artificial intelligence in education (AIED): advanced systems to support human learning

  • AIED

    • 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

      • robustness

      • effectiveness

      • context

      • change

      • resource constraints

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AIED is a Crucible for AI Research

  • AIED is AI-complete, perhaps human knowledge-complete

  • Is it tractable?

  • YES

    • 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 disciplines

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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, Cooke)

    • 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 Schwier)

    • 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??

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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

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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 beyond

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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

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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

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learning/cognitive style

previous learning objects

current goal(s)


trace of learner’s interactions

learner’s view of content

learner’s evaluation of object


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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 needs

        • 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 communities

      • to find out what happened to a learner or learners: empirical evaluation

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An Example










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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 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

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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, which essentially underlie much of the active decision making

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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

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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

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Active Learner Modelling

  • learner models aren’t stored, but are computed in context

  • 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 exist?

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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 recommendation

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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)

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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 systems

    • 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)

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The Appeal of the Ecological Vision

  • learning objects are activated: they are not passive, but take on responsibilities for their use in support of learning

  • 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 pre-defined ontology

  • approach is ecological: as end use experience accumulates, there can be an ever more refined understanding of what works for whom

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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 agent-based 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

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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

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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

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  • What works for AIED may work for many AI application areas

    • computer games, natural language understanding, AI-based e-commerce, 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 e-learning

    • 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?

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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.

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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.

    Contact me at

    [email protected]