Investigating Visitor Engagement in Interactive Science Museum Exhibits with Multimodal Bayesian Hierarchical Models

July 6th, 2020 | RESEARCH

Engagement plays a critical role in visitor learning in museums. Devising computational models of visitor engagement shows significant promise for enabling adaptive support to enhance visitors’ learning experiences and for providing analytic tools for museum educators. A salient feature of science museums is their capacity to attract diverse visitor populations that range broadly in age, interest, prior knowledge, and socio-cultural background, which can significantly affect how visitors interact with museum 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. We utilize multi-channel data (e.g., eye tracking, facial expression, posture, interaction logs) captured from visitor interactions with a fully-instrumented version of Future Worlds to model visitor dwell time with the exhibit in a science museum. We demonstrate that the proposed Bayesian hierarchical modeling approach outperforms competitive baseline techniques. These findings point toward significant opportunities for enriching our understanding of visitor engagement in science museums with multimodal learning analytics. 

Document

emerson-aied-2020.pdf

Team Members

Andrew Emerson, Author, North Carolina State University
Nathan Henderson, Author, North Carolina State University
Jonathan Rowe, Co-Principal Investigator, North Carolina State University
Wookhee Min, Author, North Carolina State University
Seung Lee, Author, North Carolina State University
James Minogue, Co-Principal Investigator, North Carolina State University
James Lester, Principal Investigator, North Carolina State University

Citation

Identifier Type: doi
Identifier: 10.1007/978-3-030-52237-7_14

Publication: Proceedings of the Twenty-First International Conference on Artificial Intelligence in Education
Page(s): 165-176

Funders

Funding Source: NSF
Funding Program: Advancing Informal STEM Learning (AISL)
Award Number: 1713545
Funding Amount: $1,951,956.00

Related URLs

Multimodal Visitor Analytics: Investigating Naturalistic Engagement with Interactive Tabletop Science Exhibits

Tags

Audience: Learning Researchers | Museum | ISE Professionals
Discipline: Climate | Ecology | forestry | agriculture
Resource Type: Conference Proceedings | Research
Environment Type: Games | Simulations | Interactives | Media and Technology | Museum and Science Center Exhibits

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This material is supported by National Science Foundation award DRL-2229061, with previous support under DRL-1612739, DRL-1842633, DRL-1212803, and DRL-0638981. Any opinions, findings, conclusions, or recommendations contained within InformalScience.org are those of the authors and do not necessarily reflect the views of NSF.

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