Enhancing Multimodal Learning through Personalized Gesture Recognition

Autor/inn/en	Junokas, M. J.; Lindgren, R.; Kang, J.; Morphew, J. W.
Titel	Enhancing Multimodal Learning through Personalized Gesture Recognition
Quelle	In: Journal of Computer Assisted Learning, 34 (2018) 4, S.350-357 (8 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Junokas, M. J.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0266-4909
DOI	10.1111/jcal.12262
Schlagwörter	Nonverbal Communication; Multimedia Instruction; Computer Assisted Instruction; Data Analysis; Recognition (Psychology); Markov Processes; Task Analysis; Comparative Analysis; Accuracy; Simulation; Models; Teaching Methods + Suchen Sie Ihr Suchwort? Non-verbal communication; Nonverbale Kommunikation; Multimediales Lernen; Computer based training; Computerunterstützter Unterricht; Auswertung; Recognition; Wiedererkennen; Markowscher Prozess; Aufgabenanalyse; Simulation program; Simulationsprogramm; Analogiemodell; Teaching method; Lehrmethode; Unterrichtsmethode
Abstract	Gestural recognition systems are important tools for leveraging movement-based interactions in multimodal learning environments but personalizing these interactions has proven difficult. We offer an adaptable model that uses multimodal analytics, enabling students to define their physical interactions with computer-assisted learning environments. We argue that these interactions are foundational to developing stronger connections between students' physical actions and digital representations within a multimodal space. Our model uses real time learning analytics for gesture recognition, training a hierarchical hidden-Markov model with a "one-shot" construct, learning from user-defined gestures, and accessing 3 different modes of data: "skeleton positions," "kinematics features," and "internal model parameters." Through an empirical comparison with a "pretrained" model, we show that our model can achieve a higher recognition accuracy in repeatability and recall tasks. This suggests that our approach is a promising way to create productive experiences with gesture-based educational simulations, promoting personalized interfaces, and analytics of multimodal learning scenarios. (As Provided).
Anmerkungen	Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01