Despite the centrality of racialized difference to evaluation, the field has yet to develop a body of literature or guidelines for practice that advance understanding of difference and inequality, including its own role therein. The purpose of this study was to broaden understanding of observed differences and inequality in evaluation beyond individuals and individual lifetimes.
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.
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 document presents the final evaluation report for the NSF-funded AISL project: "Multimodal Visitor Analytics: Investigating Naturalistic Engagement with Interactive Tabletop Science Exhibits."