Collaborative E Learning Assistant Network

© University of Reading 2007 w w w .re ading.ac.uk

Scho o l o f Syste ms Engine e ring

2 December 2007

Collaborative eLearning Assistant NetworkCaring agents are conscious agents

Joined-up learning – connectivism – The Eighth Annual Durham Blackboard Users Conference

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Introduction• The team:

Patrick Parslow, Shirley Williams, Will Browne

• Contact details:[email protected]

• My Background – – Cybernetics, Computer Science, Civil Engineering(!)


Participation!• Huge topic - Machine Consciousness (MC) &

eLearning– Philosophy, Pedagogy, Computer Science, Psychology, Sociology,

Ethics, Communities of Practice…

• Controversy about :– Whether MC is possible?– Whether MC is desirable?– Would MC improve an eLearning Assistant?– What is consciousness anyway?

• So – I will be asking for your opinions during the presentation. 3


What do I mean, ‘Consciousness’?

• It is hard to gain a consensus on what is meant by Consciousness – and hard to describe

• Features of a conscious system, by my working definition:

– Aware of surroundings– Aware of self (an autonomous entity distinct from environment)– Aware of others (as autonomous agents in the environment)– Holding a Theory of Mind of others– Having a Theory of Mind of self

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How conscious can a computer be?

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1. Not at all2. Aware of surroundings3. Aware of self4. Aware of others5. Fully

Not a

t all

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Why a conscious Assistant?• Self (1999) advocated caring intelligent tutoring

systems– Learner models– Prediction– Adaptive

• Conscious systems have– Theories of mind (models of the ‘other’)– Prediction– Adaptation– ‘Self’ awareness (!)

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Hypothesis – consciousness is an emergent property

• Based on a certain minimum functionality – Machine Consciousness Capable (MCC)– can recognise, classify, model, communicate and predict

• Community– exist in an environment with others like them

• Advantage– there is an ‘evolutionary’ advantage to modelling the ‘other’

• Model of self is a ‘freebie’– A result of associating one’s own being with other similar

agents– Using same processes that model ‘other’ to model ‘self’

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Is it ethical?

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1. No2. If it can be proven safe3. Human rights come first4. If the MC has rights5. Yes

No

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Motivation• Motivation to use in eLearning

– Caring agents need to be able to model and predict• Thus they need to perceive, recognise, classify

– Learners exist in communities• Thus paired eLearning companions can exist in

communities

– The eLearning assistant works in a ‘symbiotic’ relationship• Benefits from providing the best advantage to its partner

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Complications• Multiple strands of thought through different neural

pathways– Only aware of one at a time

• Multiple interests– Like to keep on top of them all

• Multiple roles– In different contexts, family, social, academic, professional

• Multiple domains means multiple ontologies– Or does it? Folksonomies and context awareness…

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Complexity• To deal with the complicated, use complexity.

• Not multiple MC agents, but multiple agents making up the machine consciousness– Accessing the same internal models

• Communicating with the ‘user’ or learning partner• But also with other MC agents in a network

– Bringing experience from other learners – Building and exploiting a trust network– Generating meaning through folksonomical activity

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In pictures

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Supporting Connectivism…

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Would a Machine ConsciouseLearning agent help?

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1. No2. Only some people3. Many, but not all people4. Yes

No

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Context, Meaning, Community

• First the “Alternative” view – Identity– Our roles in communities are given meaning by their context– Our identity is the aggregation of the meaning created– We define ourselves in the context of community

• Our sense of ‘self’, – the conscious feeling we are who we– defined and refined through continuous comparison, evaluation

– Consciousness takes time to develop

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Context, Meaning, Community

• All things our MCC agent needs to be able to model– All embodied to some extent in a folksonomy if :

• it records when tags were created• it records who created the tags• it allows tags to be tagged• it allows all the users resources and contacts to be tagged

• We are developing a folksonomical file system, FFS– Core technology behind the MeAggregator™, a JISC

sponsored project.

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MeAggregator™ • Designed to:

– Interact with user-owned technologies– Build folksonomies– Provide a trust network - both permission and reliability– Allow peer-peer communication and publication– Run as a server or desktop solution

http://meaggregator.googlecode.com/• Chosen as a backbone because it provides

– Ontology– Trust– Peer – Peer– Search

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Thank you

Any Questions?

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Learner model• Building models of learning partner and self

– Open learner modelling• User control• Reflective

– Both learners, in partnership• User can maintain a model of agent• Helps agent learn about itself, its partner, and the

relationship

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CeLAN• MC agents can support multiple roles.

– Given a priori domain knowledge, can be intructivist– Can work as a mentor– Can be motivational– In a network, is connectivist

• My preference?– Research assistant – assessing sources for me– Conversational – seeming interested in what I am doing– Learns the subject area with me

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Use case• Pat is researching Facebook and Blackboard, and

searches for “VLE”– CeLAN observes him choose the last link on the results page – CeLAN “Why that link?”

• I trust JISC– CeLAN adds resources and relationships to its model – resA: http://www.jiscinfonet.ac.uk/InfoKits/effective-use-of-VLEs

• relA: Pat searchedFor VLE• relB: Pat choseLink resA• relC: JISC trustedWRT relA• relD: JISC relatedTo resA (etc.)

– CeLAN interprets, and does a background search for “VLE JISC”

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Technology

Collaborative E Learning Assistant Network