Collaborative E Learning Assistant Network
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Collaborative E Learning Assistant Network



Outline presentation about project to create a networking collaborative learning assistant

Outline presentation about project to create a networking collaborative learning assistant



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Collaborative E Learning Assistant Network Collaborative E Learning Assistant Network Presentation Transcript

  • Collaborative eLearning Assistant Network Caring agents are conscious agents 2 December 2007
  • Introduction
    • The team:
    • Patrick Parslow, Shirley Williams, Will Browne
    • Contact details:
    • [email_address]
    • 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.
  • 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
  • How conscious can a computer be?
    • Not at all
    • Aware of surroundings
    • Aware of self
    • Aware of others
    • Fully
  • 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 (!)
  • 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’
  • Is it ethical?
    • No
    • If it can be proven safe
    • Human rights come first
    • If the MC has rights
    • Yes
  • 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
  • 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…
  • 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
  • In pictures
  • Supporting Connectivism…
  • Would a Machine Conscious eLearning agent help?
    • No
    • Only some people
    • Many, but not all people
    • Yes
  • 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
  • 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.
  • 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
    • Chosen as a backbone because it provides
      • Ontology
      • Trust
      • Peer – Peer
      • Search
  • Thank you
    • Any Questions?
  • 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
  • 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
  • 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:
        • 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”