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
This collaborative project seeks to address these challenges by designing, implementing, and studying an educator learning model that helps educators recognize and transform the moment-to-moment learning interactions that perpetuate racial inequalities across a myriad of STEM contexts.
This project seeks to broaden the mathematical imagination and aspirations of Black and other underserved mathematics students in both in-school and out-of-school environments.
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TEAM MEMBERS:
Erica WalkerRobin WilsonLalitha Vasudevan
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
The present project will test one strategy for improving the trustworthiness of psychological science reporting: journalists could select research based on methods rather than results (results-blind selection). This approach thus shifts the focus to prioritizing sound, rather than sensational, findings.
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TEAM MEMBERS:
Alexa Tullett
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."
The National Academy of Sciences, in partnership with the Nobel Foundation will host the second Nobel Prize Summit: Truth, Trust and Hope on May 24-26, 2023.
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TEAM MEMBERS:
Franklin Carrero-Martinez Emi Kameyama
This is a compilation of front-end, formative, and a partial summative evaluations, and an exploratory study using the xMacroscope, a data visualization technology developed for generating data from an exhibit using data captured from visitor actions.