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JTEL 2010




                                  Acquiring conceptual knowledge
                                   on how systems behave using
                                  Qualitative Reasoning technology

                                           Wouter Beek, Bert Bredeweg
                                                    W.G.J.Beek@uva.nl
                                                     B.Bredeweg@uva.nl
                                              University of Amsterdam
                                                  The Netherlands


This work is co-funded by the EC within FP7, Project no. 231526, http://www.DynaLearn.eu
Overview
•  Introduction
   –  Problem statement & context
   –  Main objective
•  Constructing conceptual knowledge
   –  Example
   –  Learning-by-conceptual-modelling
   –  Learning spaces
•  Educational components
   –    Semantic technology
   –    Virtual characters
   –    Example: quiz
   –    Science curricula
•  Evaluation & conclusion
                                         JTEL 2010
Problem statement
•  Worrying decline in science curricula
  –  Less students sign up
  –  More students drop out
•  Main reasons
  –  Lack of engagement and motivation in science teaching
  –  Teaching involves surface knowledge in terms of formulas
     and uninterpreted numeric data
  –  Lack of interactive tools to construct conceptual
     knowledge
                      e.g. Osborne et al. 2003	

                                                       JTEL 2010
Conceptual science education
Having learners acquiring conceptual knowledge
of system’s behaviour:
•  Deep knowledge in terms of the concepts that are involved
•  Learn basic principles that can be carried over to other
   problem instances
•  Learn to adequately explain and predict the behaviour of
   systems to utilise their functioning for human benefit
•  A prerequisite for working with numerical models and
   equations
•  Communicate insights to the general public

                                                          JTEL 2010
DynaLearn - Main objective
•  To develop an interactive learning environment that
   allows learners to construct their conceptual system
   knowledge, either individually or in a collaborative
   setting.
•  Strategic characteristics:
   –  Accommodate the true nature of conceptual knowledge
   –  Be engaging by using personified agent technology
   –  React to the individual knowledge needs of learners
   –  Applied to the interdisciplinary curriculum of
      environmental science
                                                       JTEL 2010
Overview
•  Introduction
   –  Problem statement & context
   –  Main objective
•  Constructing conceptual knowledge
   –  Example
   –  Learning-by-conceptual-modelling
   –  Learning spaces
•  Educational components
   –    Semantic technology
   –    Virtual characters
   –    Example: quiz & user modelling
   –    Science curricula
•  Evaluation & conclusion
                                          JTEL 2010
Example
                            F	

               m	

                                             F = m * a	


•  We say that:
   –  An increase (or decrease) in Force causes an increase (or
      decrease) in Acceleration
   –  An increase (or decrease) in Mass causes an decrease (or
      increase) in Acceleration
•  But we do not say:
   –  An increase in Acceleration causes …

                                                            JTEL 2010
Learning-by-conceptual-
                   modelling
•  Modelling is fundamental to human cognition and
   scientific inquiry (cf. Schwarz & White, 2005)
•  Simulations mimic the behaviour of real-world
   systems.
•  Conceptual Reasoning captures the human
   interpretation of reality:
   –  Couched in the appropriate vocabulary
   –  Remove numerical ‘overhead’
   –  Provides handles to automate interaction



                                                 JTEL 2010
Explicitizing the semantics of
                  the domain
•  Scope: Which aspects of the system should be
   included in the model? (relevant/irrelevant)
•  Granularity: What is the level of detail that should
   be modeled?
•  Compositionality: How must knowledge be put in
   modules in order to allow knowledge reuse?
•  Conditionality: Under what conditions do certain
   knowledge modules apply?


                                                   JTEL 2010
NaturNet-Redime - http://www.garp3.org	

Special issue: Ecological informatics, 4(5-6), 2009
JTEL 2010


              Communicative interaction



                                          Tutoring	

                                         & training	


Supporting domain	

experts with theory	

   development	

        Automated	

                         conceptual	

                                         Stakeholder	

                          reasoner	

    management
Learning spaces
1	

                                 3	

                         5	

                                           Casual 	

       Concept	

                       state graph	

               Conditional	

        map	

                      - correspondences	

             knowledge	

                                       -  magnitudes	




                2	

                              4	

                          6	

                                                       Causal	

                       Knowledge	

                    Causal model	

                                                   Differentiation	

                     Re-use	

                     -  single state	

                                                    - propagation	

                    - scenarios	

                     -  derivatives	

                                                        - rates	

                     - fragments	




                                                                                               JTEL 2010
Expressing conceptual knowledge
        (learning space 2)




                             JTEL 2010
Overview
•  Introduction
   –  Problem statement & context
   –  Main objective
•  Constructing conceptual knowledge
   –  Example
   –  Learning-by-conceptual-modelling
   –  Learning spaces
•  Educational components
   –    Semantic technology
   –    Virtual characters
   –    Example: quiz & user modelling
   –    Science curricula
•  Evaluation & conclusion
                                          JTEL 2010
Semantic technology
                                                                       Student	

Expert/teacher	

                                 http://dbpedia.org/resource/Size	

                      http://dbpedia.org/resource/Population	

                      http://dbpedia.org/resource/Mortality_rate	



                                 grounding




                            Semantic repository


                                                                           JTEL 2010
Feedback and recommendation
 e.g., “You can complete your             feedback
 model with a P+ proportionality”
                                                                       Expert	

 Student	



                                                      Community of users	





e.g., “Users who modelled           recommendations
death also modelled birth”




                                                                    JTEL 2010
Engaging virtual characters
•  Handling complex knowledge
•  Handling large amounts of knowledge in a rich
   vocabulary
•  Use different agents for different kinds of
   knowledge
•  Drama-based learning: VCs interacting with
   the learner and with one another
•  Natural Language Generation and Speech
   Synthesis                                   JTEL 2010
JTEL 2010


                      Character roles
                                                               Peer	

•    Basic help (what is, how to, ..)
•    Advanced help (model diagnosis)             Peer	



•    Teachable agent
•    Model comparison
                                                             Teacher	

•    Critic
                    Peer	

•    Quiz                           Quiz M.
JTEL 2010


Use case: Teachable agent




                       Biswas, et.al	

                      (Betty's Brain)
Science curricula
•  Interdisciplinary approach: Environmental
   science
•  Rethink the administration of the subject
   matter
•  Establish semantic repository of explanatory
   models
•  Create lesson plans
•  Blend in with ongoing classroom learning
   activities
                                             JTEL 2010
Project beneficiaries
•    University of Amsterdam (UvA - Netherlands)
•    Universidad Politécnica de Madrid (UPM - Spain)
•    University of Augsburg (UAU - Germany)
•    University of Brasília (FUB - Brazil)
•    Tel Aviv University (TAU - Israel)
•    University of Hull (UH - United Kingdom)
•    Bulgarian Academy of Sciences (CLGE - Bulgaria)
•    University of Natural Resources and Applied Life
     Sciences (BOKU - Austria)
                                                 JTEL 2010
Overview
•  Introduction
   –  Problem statement & context
   –  Main objective
•  Constructing conceptual knowledge
   –  Example
   –  Learning-by-conceptual-modelling
   –  Learning spaces
•  Educational components
   –    Semantic technology
   –    Virtual characters
   –    Example: quiz & user modelling
   –    Science curricula
•  Evaluation & conclusion
                                          JTEL 2010
Typical evaluation questions
•  Does the DynaLearn diagrammatic approach allow learners to address
   more complex problems?
•  Does the meta-vocabulary from which a conceptual interpretation is
   built, provide an analytic instrument that enables learners to construct
   more fine grained and thorough analyses of how systems work?
•  Do the embodied conversational agents establish the ‘involvement
   momentum’ required?
•  Do the instruments to individualise learning (ontology mapping,
   diagnostic procedures, and semantic repository) adequately steer
   learners in acquiring the target subject matter?
•  Does the personal autonomy cause learners to be more motivated?
•  Do learners actually learn better when using the full set of DynaLearn
   results? And are students more motivated to take on science curricula?

                                                                      JTEL 2010
Conclusions & expectations
•  Allows learners to articulate, analyse and
   communicate ideas, and thereby
   –  Construct conceptual knowledge about scientific theories
•  Engages learners in science education
•  Reduces the perceived complexity
•  Provides individualised feedback

             Ultimate research question:	

        Under which conditions will learners be	

          more motivated and achieve more?	

                                                          JTEL 2010
“Call to arms”
DynaLearn is always looking for partners that
want to:
•  Use DynaLearn in their educational practice.
•  Perform their own research using DynaLearn.
•  Contribute to enhancing Science Education in
   any other way.


         http://www.DynaLearn.eu
                                           JTEL 2010
Administrative summary
•  Project number: 231526
•  Project acronym and title: DynaLearn - Engaging and
   informed tools for learning conceptual system knowledge
•  Starting date: February 1st, 2009
•  Duration in months: 36 PMs
•  Call (part) identifier: FP7-ICT-2007-3
•  Activity code(s) most relevant to the topic: ICT-2007.4.3:
   Digital libraries and technology-enhanced learning
•  Keywords: Conceptual knowledge, Science education,
   Diagrammatic, representations, Ontology mapping, Virtual
   characters.
                       http://www.DynaLearn.eu	

                                                          JTEL 2010

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DynaLearn@JTEL2010_2010_6_9

  • 1. JTEL 2010 Acquiring conceptual knowledge on how systems behave using Qualitative Reasoning technology Wouter Beek, Bert Bredeweg W.G.J.Beek@uva.nl B.Bredeweg@uva.nl University of Amsterdam The Netherlands This work is co-funded by the EC within FP7, Project no. 231526, http://www.DynaLearn.eu
  • 2. Overview •  Introduction –  Problem statement & context –  Main objective •  Constructing conceptual knowledge –  Example –  Learning-by-conceptual-modelling –  Learning spaces •  Educational components –  Semantic technology –  Virtual characters –  Example: quiz –  Science curricula •  Evaluation & conclusion JTEL 2010
  • 3. Problem statement •  Worrying decline in science curricula –  Less students sign up –  More students drop out •  Main reasons –  Lack of engagement and motivation in science teaching –  Teaching involves surface knowledge in terms of formulas and uninterpreted numeric data –  Lack of interactive tools to construct conceptual knowledge e.g. Osborne et al. 2003 JTEL 2010
  • 4. Conceptual science education Having learners acquiring conceptual knowledge of system’s behaviour: •  Deep knowledge in terms of the concepts that are involved •  Learn basic principles that can be carried over to other problem instances •  Learn to adequately explain and predict the behaviour of systems to utilise their functioning for human benefit •  A prerequisite for working with numerical models and equations •  Communicate insights to the general public JTEL 2010
  • 5. DynaLearn - Main objective •  To develop an interactive learning environment that allows learners to construct their conceptual system knowledge, either individually or in a collaborative setting. •  Strategic characteristics: –  Accommodate the true nature of conceptual knowledge –  Be engaging by using personified agent technology –  React to the individual knowledge needs of learners –  Applied to the interdisciplinary curriculum of environmental science JTEL 2010
  • 6. Overview •  Introduction –  Problem statement & context –  Main objective •  Constructing conceptual knowledge –  Example –  Learning-by-conceptual-modelling –  Learning spaces •  Educational components –  Semantic technology –  Virtual characters –  Example: quiz & user modelling –  Science curricula •  Evaluation & conclusion JTEL 2010
  • 7. Example F m F = m * a •  We say that: –  An increase (or decrease) in Force causes an increase (or decrease) in Acceleration –  An increase (or decrease) in Mass causes an decrease (or increase) in Acceleration •  But we do not say: –  An increase in Acceleration causes … JTEL 2010
  • 8. Learning-by-conceptual- modelling •  Modelling is fundamental to human cognition and scientific inquiry (cf. Schwarz & White, 2005) •  Simulations mimic the behaviour of real-world systems. •  Conceptual Reasoning captures the human interpretation of reality: –  Couched in the appropriate vocabulary –  Remove numerical ‘overhead’ –  Provides handles to automate interaction JTEL 2010
  • 9. Explicitizing the semantics of the domain •  Scope: Which aspects of the system should be included in the model? (relevant/irrelevant) •  Granularity: What is the level of detail that should be modeled? •  Compositionality: How must knowledge be put in modules in order to allow knowledge reuse? •  Conditionality: Under what conditions do certain knowledge modules apply? JTEL 2010
  • 10. NaturNet-Redime - http://www.garp3.org Special issue: Ecological informatics, 4(5-6), 2009
  • 11. JTEL 2010 Communicative interaction Tutoring & training Supporting domain experts with theory development Automated conceptual Stakeholder reasoner management
  • 12. Learning spaces 1 3 5 Casual Concept state graph Conditional map - correspondences knowledge -  magnitudes 2 4 6 Causal Knowledge Causal model Differentiation Re-use -  single state - propagation - scenarios -  derivatives - rates - fragments JTEL 2010
  • 13. Expressing conceptual knowledge (learning space 2) JTEL 2010
  • 14. Overview •  Introduction –  Problem statement & context –  Main objective •  Constructing conceptual knowledge –  Example –  Learning-by-conceptual-modelling –  Learning spaces •  Educational components –  Semantic technology –  Virtual characters –  Example: quiz & user modelling –  Science curricula •  Evaluation & conclusion JTEL 2010
  • 15. Semantic technology Student Expert/teacher http://dbpedia.org/resource/Size http://dbpedia.org/resource/Population http://dbpedia.org/resource/Mortality_rate grounding Semantic repository JTEL 2010
  • 16. Feedback and recommendation e.g., “You can complete your feedback model with a P+ proportionality” Expert Student Community of users e.g., “Users who modelled recommendations death also modelled birth” JTEL 2010
  • 17. Engaging virtual characters •  Handling complex knowledge •  Handling large amounts of knowledge in a rich vocabulary •  Use different agents for different kinds of knowledge •  Drama-based learning: VCs interacting with the learner and with one another •  Natural Language Generation and Speech Synthesis JTEL 2010
  • 18. JTEL 2010 Character roles Peer •  Basic help (what is, how to, ..) •  Advanced help (model diagnosis) Peer •  Teachable agent •  Model comparison Teacher •  Critic Peer •  Quiz Quiz M.
  • 19. JTEL 2010 Use case: Teachable agent Biswas, et.al (Betty's Brain)
  • 20. Science curricula •  Interdisciplinary approach: Environmental science •  Rethink the administration of the subject matter •  Establish semantic repository of explanatory models •  Create lesson plans •  Blend in with ongoing classroom learning activities JTEL 2010
  • 21. Project beneficiaries •  University of Amsterdam (UvA - Netherlands) •  Universidad Politécnica de Madrid (UPM - Spain) •  University of Augsburg (UAU - Germany) •  University of Brasília (FUB - Brazil) •  Tel Aviv University (TAU - Israel) •  University of Hull (UH - United Kingdom) •  Bulgarian Academy of Sciences (CLGE - Bulgaria) •  University of Natural Resources and Applied Life Sciences (BOKU - Austria) JTEL 2010
  • 22. Overview •  Introduction –  Problem statement & context –  Main objective •  Constructing conceptual knowledge –  Example –  Learning-by-conceptual-modelling –  Learning spaces •  Educational components –  Semantic technology –  Virtual characters –  Example: quiz & user modelling –  Science curricula •  Evaluation & conclusion JTEL 2010
  • 23. Typical evaluation questions •  Does the DynaLearn diagrammatic approach allow learners to address more complex problems? •  Does the meta-vocabulary from which a conceptual interpretation is built, provide an analytic instrument that enables learners to construct more fine grained and thorough analyses of how systems work? •  Do the embodied conversational agents establish the ‘involvement momentum’ required? •  Do the instruments to individualise learning (ontology mapping, diagnostic procedures, and semantic repository) adequately steer learners in acquiring the target subject matter? •  Does the personal autonomy cause learners to be more motivated? •  Do learners actually learn better when using the full set of DynaLearn results? And are students more motivated to take on science curricula? JTEL 2010
  • 24. Conclusions & expectations •  Allows learners to articulate, analyse and communicate ideas, and thereby –  Construct conceptual knowledge about scientific theories •  Engages learners in science education •  Reduces the perceived complexity •  Provides individualised feedback Ultimate research question: Under which conditions will learners be more motivated and achieve more? JTEL 2010
  • 25. “Call to arms” DynaLearn is always looking for partners that want to: •  Use DynaLearn in their educational practice. •  Perform their own research using DynaLearn. •  Contribute to enhancing Science Education in any other way. http://www.DynaLearn.eu JTEL 2010
  • 26. Administrative summary •  Project number: 231526 •  Project acronym and title: DynaLearn - Engaging and informed tools for learning conceptual system knowledge •  Starting date: February 1st, 2009 •  Duration in months: 36 PMs •  Call (part) identifier: FP7-ICT-2007-3 •  Activity code(s) most relevant to the topic: ICT-2007.4.3: Digital libraries and technology-enhanced learning •  Keywords: Conceptual knowledge, Science education, Diagrammatic, representations, Ontology mapping, Virtual characters. http://www.DynaLearn.eu JTEL 2010