The Ecological Approach to E-Learning

1. The Ecological Approach to E-Learning Gord McCalla ARIES Laboratory Department of Computer Science University of Saskatchewan Saskatoon, Saskatchewan CANADA

2. 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

3. 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?

4. 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

5. 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

6. 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??

7. 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

8. 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

9. 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

10. 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

11. CHARACTERISTICS personal Learner Model Instance 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

12. 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

13. An Example x x x x x ? x x ?

14. 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

15. 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

16. 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

17. 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

18. An I-Help Screen

19. HA WEB Pub MATCHMAKER I-Help ? ? ?

20. 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?

21. 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

22. 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)

23. 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)

24. 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

25. 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

26. 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

27. 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

28. Conclusion • 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?

29. Questions, Comments, Interactions? 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 • private sector support: TRLabs, Parchoma Consulting Ltd.

30. 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.http://www-jime.open.ac.uk/2004/1 • 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 mccalla@cs.usask.ca