Measuring Model-Based Learning of Complex Systems with Multiple Data Streams

Martin, Kit

doi:https://doi.org/10.21985/n2-mtf3-ea58

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Measuring Model-Based Learning of Complex Systems with Multiple Data Streams

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The dissertation builds on my current research to demonstrate the connection between affect and learning through machine learning and qualitative analysis of interactions where players use a complex systems game. The project is threefold: First, I developed a thinking and learning intervention, the agent-based modeling simulation Ant Adaptation. I showed that the intervention can shift people's schemas from a process schema to an emergent schema during 10-minute museum interactions. Second, to track that conceptual change common in agent-based modeling interventions, I developed a novel form of concept mapping, constructivist dialogue mapping (CDM), which is particularly useful as a learning analytic. Through CDM, I analyzed participants’ spoken elaborations in small subsets to study how people develop their understanding of a system or museum exhibit over time. Third, through video analysis, I developed a method of affect detection to identify how participants are engaged across 17 facial action units. I map those affective-states to moments of learning using machine learning methods. Because the data source is video, the method has outsized potential for scale to predict unseen data. In short, in my work I have applied advanced methods to the design and evaluation of educational interventions.

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