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Fig. 2 Overview of the dynamic gesture analysis approach proposed.
Let us now investigate how such features can be used for dynamic gesture analysis.
They found that transformational speech and dynamic gesture independently accounted for unique variance in their model predicting proof validity.
But the pilot yields mixed results for deriving in-game interventions intended to elicit dynamic gesture production.
H6 (as with H1, above) examines the theoretical process account that proof-with-insight performance is associated with dynamic gesture production.
The proposed dynamic gesture analysis method is illustrated in Fig. 2, with both off-line (learning) and on-line (classification) stages.
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This supports H1, a novel process claim, which stated that dynamic gestures would predict mathematical insight.
Performing mathematically relevant directed actions (without pedagogical language) will predict the production of dynamic gestures.
Dynamic gestures, to review, are those that manually depict and transform an object.
In sum, we see the predicted association between dynamic gestures and insight (H1).
Production of dynamic gestures during proof and justification will predict student performance on mathematical insights.
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