This guide was created by adapting the Sciencenter PD materials for broader dissemination. It is intended to provide some general information and tips on incorporating more youth voices in an ISL setting, as well as a framework for convening discussions with others at your institution around the topic.
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resourceresearchMuseum and Science Center Programs
Youth Lead the Way: A Youth Advisory Research Board Model for Climate Impact Education, hosted by the Oregon Museum of Science and Industry (OMSI), offered a theory-based approach for youth from communities underrepresented in STEM to conduct content research on local climate change impacts and develop interactive educational products designed to engage public audiences around these impacts. Through the Youth Lead the Way project, a program that supports youth and science center collaboration was developed and implemented by integrating two well-established methods: Youth Advisory Boards and
This is a guide to supporting a Youth Advisory Research Board, abbreviated to “YARB.” A YARB integrates two well-established methods of working with youth: Youth Advisory Boards and Youth Participatory Action Research. Youth Advisory Boards give young participants an opportunity to implement real, observable change at institutions such as informal science education centers.
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Oregon Museum of Science and Industry
resourceresearchMuseum and Science Center Programs
This guide outlines how to tackle potential gaps in communication, engagement, scheduling, and work styles, as well as provide different ways to incorporate youth input and voice into projects. This guide is divided into four sections: Youth vs. Adults, Youth Engagement, Communicating with Youth, and Advising.
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