Enhancing multimodal learning through personalized gesture recognition.
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...
| Publicado en: | Journal of Computer Assisted Learning Vol. 34; no. 4; pp. 350 - 358 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130898734&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130898734 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2018 vid: 34 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 130898734 130898734 130898734 10.1111/jcal.12262 130898734 ppf: 350 ppct: 8 formats: tig: atl: Enhancing multimodal learning through personalized gesture recognition. aug: au: Junokas, M. J. Lindgren, R. Kang, J. Morphew, J. W. affil: University of Illinois, Urbana‐Champaign, United States sug: subj: Motion Analysis Systems Computer-Assisted Instruction Nonverbal Communication Human Kinematics Adolescence Adult Female Male Task Performance and Analysis Experimental Studies T-Tests Funding Source Adolescent: 13-18 years Adult: 19-44 years Female Male ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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