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 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 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."
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.
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
resourceresearchMuseum and Science Center Exhibits
The executive summary of the Formative Research Report for the project: Fostering Joint Parent/Child Engagement in Preschool Computational Thinking by Leveraging Digital Media, Mobile Technology, and Library Settings in Rural Communities.
This is the formative research report for the project: Fostering Joint Parent/Child Engagement in Preschool Computational Thinking by Leveraging Digital Media, Mobile Technology, and Library Settings in Rural Communities