We present the assets that collaboration across a land grant university brought to the table, and the Winterberry Citizen Science program design elements we have developed to engage our 1080+ volunteer berry citizen scientists ages three through elder across urban and rural, Indigenous and non-Indigenous, and formal and informal learning settings.
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TEAM MEMBERS:
Katie SpellmanJasmine ShawChristine VillanoChrista MulderElena SparrowDouglas Cost
We used a youth focused wild berry monitoring program that spanned urban and rural Alaska to test this method across diverse age levels and learning settings.
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TEAM MEMBERS:
Katie SpellmanDouglas CostChristine Villano
The Arctic Harvest-Public Participation in Scientific Research (which encompasses the Winterberry Citizen Science program), a four-year citizen science project looking at the effect of climate change on berry availability to consumers has made measurable progress advancing our understanding of key performance indicators of highly effective citizen science programs.
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TEAM MEMBERS:
Angela LarsonKelly KealyMakaela Dickerson
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
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."