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QUALITATIVE MODELING IN EDUCATION

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QUALITATIVE MODELING IN EDUCATION. Bert Bredweg and Ken Forbus. Yesim Imamoglu ... What happens, when does it happen, what effects it, what does it effect? ... – PowerPoint PPT presentation

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Title: QUALITATIVE MODELING IN EDUCATION


1
QUALITATIVE MODELING IN EDUCATION
  • Bert Bredweg and Ken Forbus
  • Yesim Imamoglu

2
Computers and Education
  • Using educational software
  • Supports constructivism in education
  • Allows multiple representations and interaction
  • Serves to individual differences

3
Qualitative Reasoning and Education
  • Qualitative reasoning is valuable for education
    because
  • Uses of conceptual knowledge
  • What happens, when does it happen, what effects
    it, what does it effect?
  • Provides grounding and framework for quantitative
    and traditional mathematical models

4
Recent Applications
  • Teachable Agents project (Vanderbilt University,
    Biswas et al. 2001)
  • Bettys Brain-qualitative mathematics
  • ALI (DSauza et al. 2001)
  • Based on qualitative process theory
  • Domain independent-can be attached to any
    quantitative simulation

5
Recent Applications
  • Application of qualitative process theory in
    chemistry classes (Syedd, Pang and Sharifuddin,
    2002)
  • Qualitative model is used alongside classroom
    experiment to determine optimal use of substances

6
Aspects of modeling that existing visual
languages do not address
  • The importance of broadly applicable principles
    and processes
  • Understanding when a model is relevant
  • Qualitative understanding of behavior

7
VModel
  • Student friendly visual notation for qualitative
    process theory
  • Creates a software environment that helps
    students express their qualitative, conceptual
    models

8
VModel
  • Visual notation is based on concept maps
  • Nodes represent entities and properties of
    entities
  • Each node has a specified type such as Thing,
    Multiple Thing, Substance or Process
  • Quantities are used to describe the continuous
    properties of entities
  • Links represent relationships, labels are drawn
    from a fixed set of relationships

9
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10
VModel
  • Small, fixed set of structural relationships
  • Touches, Contains, Part of...
  • Ordinal relationships
  • Greater Than, Less Than, Equal To
  • Requires relationship links a process to the
    conditions that enable it
  • Casual relationships
  • Increases, Decreases direct influences (I/I-)
  • Influences, InfluencesOpposite qualitative
    proportionalities

11
VModel
  • Two coaches
  • Qualitative simulation to help the students see
    how well their model make predictions
  • Modeling equivalent of spelling and grammar
    checking
  • Three sources of feedback
  • Visual step-by-step animation of the simulation
  • English summary of behavior predicted by the
    model
  • Assessment of how well the model supports the
    hypothesis

12
VModel
  • Model library contains all the models that the
    students have created
  • Students can build their own domain theory

13
HOMER and VISIGARP
  • Allow learners to use a qualitative reasoning
    engine for running and inspecting simulations
  • They work on top of the domain independent
    qualitative reasoning engine GARP
  • Use diagrammatic reprsentations for buliding and
    inspecting qualitative models and simulations

14
Model Building with HOMER
  • Organized set of builders and tools
  • Builders
  • Capture knowledge and use diagrammatic
    representations
  • Used for creating building blocks (entitiy
    hierarchy, quantities, quantity spaces...)
  • Tools Interactive dialogues for modifying the
    content of builders
  • Constructs
  • Model fragments and scenarios
  • Assembled from building blocks

15
Model Building with HOMER
  • Task
  • Create a set of model fragments (stored in
    library)
  • Specify one or more scenarios (structural
    description of the system)
  • Model Building
  • Simulator uses the model fragments to predict the
    behavior of the system defined in the selected
    scenario
  • For each of the specified scenarios, the
    simulator generates the intended behavior graph

16
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17
HOMER Prevents learners from making syntatically
incorrect models
  • The user interface restricts user actions based
    on
  • Content
  • Current selections in the builder that the user
    is working on
  • Investigates each user action with respect to
    side-effects
  • Notifies the user
  • Gives user the option either carry on with the
    action or cancel it

18
Problems encountered using HOMER
  • Homer was tested on two researchers and two
  • master students from a computer science
  • department
  • Problems caused by poor use of the tool
  • Problems caused by subjects not fully
    understanding how to perform a task
  • (model building problems)

19
Model building problems
  • Model scope
  • Determining which features of the real-world
    sytem to include in the model
  • Model Structure
  • Determining what to put where in the model
  • Model Building Concepts
  • Understanding the meaning and difference between
    the concepts provided by the tool
  • Model Representations
  • Knowing the concept, what to represent but not
    knowing how to represent it

20
VISIGARP
  • Provides a graphic interface to for running and
    inspecting qualitative simulations
  • Simulations might use models constructed by
  • teachers
  • domain experts
  • the learners (for example, by using HOMER)

21
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22
VISIGARP
  • Visual container All model ingredients belonging
    to a particular area are grouped into a single
    box
  • Ingredients relating aspects from different
    entities cross the border of the boxes

23
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24
Usability of VISIGARP
  • Experiment was conducted to 30 first year
    university (psychology) students
  • Pretests and posttests were conducted on domain
    knowledge and icon language of VISIGARP
  • Treatment Prediction exercises using simulations
  • Subjects were asked to evaluate VISIGARP after
    treatment

25
Usability of VISIGARP
  • Results indicate that
  • There was significant difference between pre/post
    tests in domain knowledge
  • No significant difference between pre/post tests
    in icon language
  • Most icons were easy to learn , lt, , , P,
    P-, I, I-
  • A few icons were hard to understand Q, Q, V, V
    (they were not used much during simulation)
  • VISIGARP did not always produce insighful graphs
    for comlex models
  • Attitude test revealed that subjects evalated the
    usefulness of VISIGARP quite positively

26
Conclusion
  • Importance of conceptual knowledge in education
  • Reasoning about system behavior
  • Two qualitative models VModel and HOMER/VISIGARP
    combination
  • Both foster learning as a constructive process

27
Comments
  • Experiments are done with subjects at the
    university level
  • Further adjustments may be needed if it is going
    to be used in high-middle school level (more user
    friendly)
  • Suitable for the new curriculum in Turkey, but
    not easy to put into practice
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