The goal of this evaluation was to determine how museum visitors responded to the museum's existing live animal exhibits and identify recommendations for their new Live Animal Garden exhibit.
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
An adapted three-dimensional model of place attachment is proposed as a theoretical framework from which place-based citizen science experiences and outcomes might be empirically examined in depth.
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TEAM MEMBERS:
Julia ParrishYurong HeBenjamin Haywood
This research examines the Tree Investigators project to support science learning with mobile devices during family public programmes in an arboretum. Using a case study methodology, researchers analysed video records of 10 families (25 people) using mobile technologies with naturalists at an arboretum to understand how mobile devices supported science talk related to tree biodiversity. The conceptual framework brings together research on technological supports for science learning and research on strategies that encourage families to engage in conversations that support observation and
This poster was presented at the 2021 NSF AISL Awardee Meeting.
Dinosaurs of Antarctica is a giant screen film and outreach project that documents the work of NSF-funded researchers on expeditions to Shackleton Glacier during the 2017-2018 field season. This immersive film and companion television special will bring the past to life and engage the public, and particularly students in middle grades (6-9), with polar science through appealing, entertaining media experiences and informal learning programs. The film serves as a companion for the synonymous Antarctic Dinosaurs museum exhibition