Researchers at Arizona State University (ASU), in partnership with the Smithsonian Museum on Main Street (MoMS), the Arizona Science Center, and eight tribal and rural museum sites around Arizona, will help educate and empower communities living in the Desert Southwest on water sustainability issues through the creation of WaterSIMmersion, a mixed reality (MR) educational game and accompanying museum exhibit.
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TEAM MEMBERS:
Claire LauerScotty CraigMina Johnson-GlenbergMichelle Hale
Successful peer-to-peer practices in informal science learning (ISL) are often not well defined, but further investigation has the potential to help uncover how to motivate and scaffold children's joint learning in science and engineering. Team Hamster!, a PBS KIDS interactive digital series that helps youth think creatively and use engineering skills to solve problems with everyday tools, will be used to achieve the goals of this project.
This document is the final evaluation report for the project, which focuses both on formative evaluation of the collaborative+interdisciplinary presentation creation process and summative evaluation of audience learning outcomes.
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