Early Prediction of Museum Visitor Engagement with Multimodal Adversarial Domain Adaptation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).

Autor/inn/en	Henderson, Nathan; Min, Wookhee; Emerson, Andrew; Rowe, Jonathan; Lee, Seung; Minogue, James; Lester, James
Titel	Early Prediction of Museum Visitor Engagement with Multimodal Adversarial Domain Adaptation [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021).
Quelle	(2021), (12 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Museums; Audiences; Participation; Exhibits; Interaction; Learning Modalities; Multimedia Instruction; Computer Simulation; Preadolescents; Learner Engagement; Prediction; Educational Technology; Technology Uses in Education; Video Technology; North Carolina + Suchen Sie Ihr Suchwort? Museum; Museumswesen; Museen; Spectator; Zuschauer; Teilnahme; Interaktion; Lernumgebung; Multimediales Lernen; Computergrafik; Computersimulation; Pre-adolescence; Präadoleszenz; Vorhersage; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen
Abstract	Recent years have seen significant interest in multimodal frameworks for modeling learner engagement in educational settings. Multimodal frameworks hold particular promise for predicting visitor engagement in interactive science museum 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. This approach enables the use of pre-trained multimodal visitor engagement models in circumstances where multimodal instrumentation is not available. We evaluate the visitor engagement models in terms of early prediction performance using exhibit interaction and facial expression data captured during visitor interactions with a science museum exhibit for environmental sustainability. Through the use of modality-invariant data representations generated by the adversarial discriminative domain adaptation framework, we find that pre-trained multimodal models achieve competitive predictive performance on interaction-only data compared to models evaluated using complete multimodal data. The multimodal framework outperforms unimodal and non-adapted baseline approaches during early intervals of exhibit interactions as well as entire interaction sequences. [For the full proceedings, see ED615472.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01