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
resourceresearchMuseum and Science Center 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
Snow: Tiny Crystals, Global Impact is a 2,500 ft traveling exhibition about: how snow shapes and sustains life on Earth, the impacts of climate change on snow, and the importance of our collective engagement to take action. The exhibition will be installed at OMSI during winter 2021-2022 for summative evaluation and learning research.
This poster was presented at the 2021 NSF AISL Awardee Meeting.
This poster was presented at the 2021 NSF AISL Awardee Meeting.
This project employs youth (ages 16-21) from frontline communities to work in paid positions as purveyors of climate science, develop communication and leadership skills, and engage in timely conversations with members of the public about climate change impacts in their own communities. The youth work in small groups to develop an educational tool based in personal narrative and current climate science as a way to raise public understanding and awareness about local climate impacts. They also act as advisors and colleagues in
This handout was prepared for the Climate Change Showcase at the 2019 ASTC Conference in Toronto, Ontario. It highlights resources available on InformalScience.org related to the topic of climate change.
This poster, which was presented in Alexandria, VA at the CAISE AISL PI meeting in February 2019, summarizes the Under the Arctic: Digging into Permafrost traveling exhibition developed for the Hidden World of Permafrost project.