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									Representation and Interaction Design for
Effective High School Chemistry Simulations

            Suggestions from the Field
           Catherine Milne and Jan Plass
     Bruce Homer, Trace Jordan, Ruth Schwartz,
     Elizabeth Hayward, Yoo Kyung Chang, Yan
     Wang, Florrie Ng, Dixie Ching, Mubina Khan
Model-based Inquiry

   • Science is about addressing why questions
   • Explanations of scientific phenomena require an
     understanding of why
   • Simplistic approaches to scientific inquiry focus on
     descriptive correlations: what questions rather than
     why questions
   • What questions require recall of information
   • Why questions require models; theories
Model-based Inquiry

   • Models give students an explanatory framework for
     their observations
   • Models provide a generative environment for the
     development of new ideas
   • Multimedia model-based simulations offer a
     strategy for incorporating models into science
Representation and Interaction

   • Designing a simulation requires numerous decisions
      –   Choice of explanatory framework (model)
      –   Selecting phenomena
      –   Supporting narratives
      –   Approach to symbolism (e.g., graphs; text)
      –   Design of interactive features
   • These decisions have an impact on design,
     development, and evaluation of simulations
Representation and Interaction

   • The challenge of representations
   • Designing interactivity
The Challenge of Representations

    Beginning science learners versus experts
    Value of models
The Challenge of Representations

   • Simulation design
Our Simulation Design
Simulation design

   • Explanatory Framework (model)
   • Symbolic representation (graphs and text)
   • Phenomenon (narrative)
Phenomenon and Model

  – Using a narrative to
    make connections
    phenomenon, model,
    and graph
  – Using a narrative to
    provide a context for
    asking why questions
Model (Explanatory Framework) and
Graph (Symbolic Representation)
Exploring Connection Between Phenomenon
and Model

     Pilot Study – narrative, expository
        • Process data showed that narrative group spent more
          time adjusting variables and resetting graph
        • Narrative showed potential for supporting transfer
Further Scaffolding

   • Narrative
      – Exploring different types of narrative
      – Allowing students to choose different introductory narratives
      – Including ‘game-like’ elements in the narratives
   • Visual
      – What is the precise nature of visual scaffolds that should be
   • Meta-cognitive
      – Scaffolding learners to generalize rules, draw conclusions
Representation and Interaction

   • The challenge of representations
   • Designing interactivity
Designing Interactivity

1. Icons   vs. symbols – reduce cognitive load
2. Worked-out vs. Exploratory
Designing Interactivity: Studies

   • Experimental studies:
      – Low prior knowledge learners had better learning outcomes
        (recall, comprehension, transfer)with icons, while high prior
        knowledge learners had better learning outcomes without
        icons (Homer & Plass, 2010).
      – With low executive function there was no significant
        difference between worked-out and exploratory, but with
        high executive function there was a significant difference
        between the two interaction formats
Designing Interactivity: Studies

   • Efficacy study: Curriculum integration of simulations
     with lesson plans
      – Cluster analysis: students scoring poorly on the pre-test and
        much better on the post-test, were 2.5 times more likely to be
        from the treatment (with simulations) group
Representation and Interaction

   • Challenges
Challenges – Understanding interaction between learners
and simulation design

     • To what extent are specific design features used by
       the learners?
         – Do students really look at the graph or do they ignore it?
         – Do students connect graph and simulation? What can we do
           to support them to interrogate graphs and develop a
           generalized understanding using theory and relationships
           between variables that they can apply to different contexts?
         – Do students notice icons and understand their meaning?
     • Method of investigation:
         – Think-aloud protocols
         – Eye tracking
  Eye Tracker Data

When subjects looked at graphs longer and more often, they had
higher post-test scores. This was true regardless of their pre-test
score, meaning regardless of the subjects level of prior knowledge,
more frequent and longer visual reference to the graph helped them
learn better.
Challenges – Understanding which features better             support
learners’ interactions with levels of representation

     • To what extent would additional features promote
       learning (compared to cost involved)?
         –   Design your own simulation?
         –   Integration of authentic data sets?
         –   Collaboration tools?
         –   Portfolio/research notebook feature?
         –   Manipulation of graph / fitting of curves representing different
             relations of variables
     • Always keeping in the forefront of our thinking the
       question of the educational purpose served by any
Challenges – Rethinking the interaction between learning
and assessment

    • How can simulation-based learning best be studied?
        – Measuring engagement
        – Assessing learning process using process data
        – Assessing learning outcomes – beyond knowledge post tests
             –   Graph comprehension
             –   Metacognitive Strategies / Self-regulation of learning
             –   Knowledge about models
             –   Knowledge about scientific inquiry process
             –   Integration of assessment and learning e.g. assessment as learning

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