Suggestions from the Field

Catherine Milne and Jan Plass

with

Bruce Homer, Trace Jordan, Ruth Schwartz, Elizabeth Hayward, Yoo Kyung Chang, Yan Wang, Florrie Ng, Dixie Ching, Mubina Khan

Representation and Interaction Design for Effective High School Chemistry Simulations

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 learning

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 expertsValue 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

Integrating representations– Using a narrative to

make connections between phenomenon, model, and graph

– Using a narrative to provide a context for asking why questions

Model (Explanatory Framework) and Graph (Symbolic Representation)

http://create.alt.ed.nyu.edu/molecules/idealgaslaws.html•  

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

included?

• 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 intervention

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