The aim of this evaluation study was to document the progress and improvement of the 16-month Youth Lead the Way program. Specifically, the goal of the evaluation strand was to gather evidence of how the Youth Lead the Way experience provided opportunities to elicit in youth skills that aligned with the project and youth’s priorities. The evaluation team used qualitative data that varied across each of the three evaluation phases. Data were gathered periodically through surveys, concept maps, and interviews from July 2021 to August 2022.
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.
This Integrating Research and Practice project leverages museum exhibits as unique family learning spaces to promote community engagement in critical climate change conversations.
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
The Arctic is warming four times faster as a result of climate change than any other region, but the impacts of this warming are not well known beyond the local communities in the region. The Alaska Pacific University (APU) will organize a one-year planning project to further develop relationships with four Indigenous communities along the Alaskan Yukon River who are experiencing environmental and social impacts from the climate crisis.
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TEAM MEMBERS:
James TemteJulie Brigham-GretteBlane De St CroixErin Marbarger
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