This guide shares some of the successes and challenges behind the Science Museum of Minnesota’s Cardboard City exhibition and our partnership with museums across the country through Cardboard Collaborative.
The Cardboard Collaborative is the product of 10 years of work at the Science Museum of Minnesota and part of a larger collaboration with local community organizations to center BIPOC family priorities and experiences. This guide is intended to share what they have learned and support others to create their own cardboard maker worlds.
This radical volume addresses these circular discourses and reveals the gaps in the field. Putting the spotlight on the marginalised voices of so-called 'racialised minorities', and those from Global South regions, it interrogates the global footprint of the science communication enterprise.
This book is about scientific inquiry. Designed for early and mid-career researchers, it is a practical manual for conducting and communicating high-quality research in (mathematics) education.
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TEAM MEMBERS:
James HiebertJinfa CaiStephen HwangAnne K MorrisCharles Hohensee
Despite the centrality of racialized difference to evaluation, the field has yet to develop a body of literature or guidelines for practice that advance understanding of difference and inequality, including its own role therein. The purpose of this study was to broaden understanding of observed differences and inequality in evaluation beyond individuals and individual lifetimes.
There has been an increased push for science, technology, engineering, and mathematics (STEM) students and scientists to be trained in science communication. Science communication researchers have outlined various models of how scientists interact with nonscientists—including deficit, dialogue, and inclusive approaches. We wanted to analyze whether published science communication curricula for STEM students and scientists exhibit features of inclusive science communication.
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TEAM MEMBERS:
Randy VickeryKatlyn MurphyRachel McMillanSydney AlderferJasmine DonkohNicole Kelp
resourceresearchMuseum and Science Center Exhibits
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