Abstract
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. We investigate trajectories of multimodal data captured during visitor interactions with the exhibit through slope-based time series analysis. Utilizing the slopes of the time series representations for each multimodal data channel, we conduct an ablation study to investigate how additional modalities lead to improved accuracy while modeling visitor engagement. We are able to enhance visitor engagement models by accounting for varying levels of visitors’ science fascination, a construct integrating science interest, curiosity, and mastery goals. The results suggest that trajectory-based representations of the multimodal visitor data can serve as the foundation for visitor engagement modeling to enhance museum learning experiences.
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Acknowledgements
The authors would like to thank the staff and visitors of the North Carolina Museum of Natural Sciences. This research was supported by the National Science Foundation under Grant DRL-1713545. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Emerson, A., Henderson, N., Min, W., Rowe, J., Minogue, J., Lester, J. (2021). Multimodal Trajectory Analysis of Visitor Engagement with Interactive Science Museum Exhibits. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_27
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