Climate change presents a significant challenge for parents worldwide as they navigate the task of preparing the next generation for a rapidly changing world. This interdisciplinary project aims to address this challenge by focusing on the needs of under-resourced Latino families, with a particular emphasis on Latino children who bear a disproportionate burden from climatic changes.
This analysis examines how assembly-style making activities support creative expression and early engineering learning. We suggest makerspace designers and educators consider include assembly-style making activities in the mix of options available to support makers who are less comfortable with making initially.
In a unique collaboration between PBS NewsHour and Indij Public Media (the parent company of ICT, formerly known as Indian Country Today), this project will put the perspectives and the reporting of Indigenous communities front and center through their co-creation of digital and broadcast segments.
The purpose of this project is to establish and foster a new partnership between the University of Alabama and Arts 'n Autism, a community organization that provides supervised after-school care and outreach to children and youth with a diagnosis of Autism Spectrum Disorder (ASD).
This report summarizes findings from the learning event and includes the two instruments developed as part of this project: The STEM Advocacy Survey which is a 36-item measure that includes four subscales that measure components of STEM Advocacy, including Value of STEM for Society, Knowledge of STEM Advocacy, STEM Advocacy Efficacy, and STEM Advocacy Identification; and the STEM Engagement Survey for Older Adults, a ten-item scale adapted for older populations from a previously developed instrument designed for youth (ActivationLab.org) measuring behavioral, cognitive, and affective
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TEAM MEMBERS:
Jennifer MangoldSarah OlsenCheryl BrewsterMatthew Cannady
resourceprojectProfessional Development, Conferences, and Networks
The conference will provide a critical opportunity for enhancing knowledge around innovation in these areas and sharing lessons learned with and advancing collaboration. The focus will be on collective impact, rural empowerment, and successful rural STEM programs.
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