EXP: Collaborative Research: Extracting Salient Scenarios from Interaction Logs (ESSIL)

9/1/16-8/31/18

PIs: Andee Rubin, TERC Inc (Award Details)
Barbara Grosz, Harvard University (Award Details)
Leilah Lyons, New York Hall of Science (Award Details)

The Extracting Salient Scenarios from Interaction Logs (ESSIL) project proposes to develop a new type of educational technology to support students’ learning about complex systems from their participation in a multi-person immersive simulation. Many important challenges we face today as a society — including responding to climate change, managing global economies, city planning, disease outbreaks — are “complex systems” problems, meaning that important phenomena in each (for instance trends in weather, stock bubbles, traffic jams, disease transmission) result not from a single cause, but because many small causes combine together. Participating in a simulation has the potential to help students understand the principles of complex systems, but because different principles surface depending on how each simulation unfolds, it can be difficult for teachers to adjust their lesson plans on the fly to highlight the principles that emerge in a given simulation run. To address this challenge, ESSIL will develop methods to create “automatic salient recaps,” as a way to help learners and their teachers make better sense of simulations. These recaps, which will be automatically generated, provide a story of “what happened” in the simulation in a way that both helps students remember their experience and reveals important scientific principles. Teachers and other facilitators will use these recaps, along with an accompanying discussion guide, to support productive learning conversations about the scientific principles incorporated in a simulation. The recaps will be developed for a large-scale immersive simulation installed at the New York Hall of Science (NYSCI), potentially improving the educational experience of thousands of daily visitors. The capabilities developed to produce them have widespread applicability, because logs of student interactions are routinely produced by many educational systems.

The immersive simulation context for the project is Connected Worlds, an embodied, multi-person ecology simulation at NYSCI, with pedagogical goals around sustainability and systems thinking. Using logs from groups of students interacting with Connected Worlds, ESSIL will construct selective recaps of their experience that both are personally salient to them (by including memorable details of their experience) and have explanatory coherence (to enable their discussion of important interconnections in the simulation’s underlying model). Artificial Intelligence-based methods will be developed to 1) identify salient changes in the state of the simulation during student interaction and 2) construct qualitative models of causal chains that could have led to these changes. These qualitative models will be used to generate salient recaps and discussion guides based on them, which will be provided to teachers whose classes are visiting NYSCI. The effectiveness of the innovation will be investigated by comparing visiting students’ conversations with and without ESSIL-generated discussion supports and by interrogating their resulting models of the Connected Worlds system through concept maps.

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