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

Abstract

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.