Recent advances in multimodal learning analytics show significant promise for addressing these challenges by combining multi-channel data streams from fully-instrumented exhibit spaces with multimodal machine learning techniques to model patterns in visitor experience data. We describe initial work on the creation of a multimodal learning analytics framework for investigating visitor engagement with a game-based interactive surface exhibit for science museums called Future Worlds.
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TEAM MEMBERS:
Jonathan RoweWookhee MinSeung LeeBradford MottJames Lester
resourceresearchMuseum and Science Center Exhibits
Multimodal models often utilize video data to capture learner behavior, but video cameras are not always feasible, or even desirable, to use in museums. To address this issue while still harnessing the predictive capacities of multimodal models, we investigate adversarial discriminative domain adaptation for generating modality-invariant representations of both unimodal and multimodal data captured from museum visitors as they engage with interactive science museum exhibits.
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TEAM MEMBERS:
Nathan HendersonWookhee MinAndrew EmersonJonathan RoweSeung LeeJames MinogueJames Lester
resourceresearchMuseum and Science Center Exhibits
Recent years have seen a growing interest in investigating visitor engagement in science museums with multimodal learning analytics. Visitor engagement is a multidimensional process that unfolds temporally over the course of a museum visit. In this paper, we introduce a multimodal trajectory analysis framework for modeling visitor engagement with an interactive science exhibit for environmental sustainability.
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TEAM MEMBERS:
Andrew EmersonNathan HendersonWookhee MinJonathan RoweJames MinogueJames Lester
resourceresearchMuseum and Science Center Exhibits
In this paper, we introduce a Bayesian hierarchical modeling framework for predicting learner engagement with Future Worlds, a tabletop science exhibit for environmental sustainability.
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TEAM MEMBERS:
Andrew EmersonNathan HendersonJonathan RoweWookhee MinSeung LeeJames MinogueJames Lester
resourceevaluationMuseum and Science Center Exhibits
This document presents the final evaluation report for the NSF-funded AISL project: "Multimodal Visitor Analytics: Investigating Naturalistic Engagement with Interactive Tabletop Science Exhibits."
In this paper, we investigate bias detection and mitigation techniques to address issues of
algorithmic fairness in multimodal models of museum visitor visual attention.
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TEAM MEMBERS:
Halim AcostaNathan HendersonJonathan RoweWookhee MinJames MinogueJames Lester
resourceresearchMuseum and Science Center Exhibits
The executive summary of the Formative Research Report for the project: Fostering Joint Parent/Child Engagement in Preschool Computational Thinking by Leveraging Digital Media, Mobile Technology, and Library Settings in Rural Communities.
This is the formative research report for the project: Fostering Joint Parent/Child Engagement in Preschool Computational Thinking by Leveraging Digital Media, Mobile Technology, and Library Settings in Rural Communities
This project will teach foundational computational thinking (CT) concepts to preschoolers by creating a mobile app to guide families through sequenced sets of videos and hands-on activities, building on the popular PBS KIDS series Work It Out Wombats!
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TEAM MEMBERS:
Marisa WolskyJanna KookJessica Andrews
This project will use a complex systems approach to hazard reduction across multiple scales of risk by developing a new generation of socio-technical digital twin that integrates models of physical infrastructure systems and virtual networks of communication with social games to engage community awareness and commitment to collective action.
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TEAM MEMBERS:
Kenichi SogaLouise ComfortStephen CollierMichael GollnerJ. Keith Gilless