Fusing theory-guided machine learning and bio-sensing: considering time in how children learn science from dynamic multimedia.
A new era of message processing research will emerge from the convergence of powerful machine learning algorithms with dynamic data from everyday devices equipped with biological sensors. Our study takes critical steps into this era by integrating theory-guided artificial neural networks with eye mo...
| Publicado en: | Journal of Communication Vol. 76; no. 1; pp. 60 - 78 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Oxford University Press / USA
Feb2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192099674&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192099674 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00219916 JCO jtl: Journal of Communication issn: 00219916 maglogo: N pubinfo: dt: Feb2026 vid: 76 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192099674 10.1093/joc/jqaf036 ppf: 60 ppct: 18 formats: tig: atl: Fusing theory-guided machine learning and bio-sensing: considering time in how children learn science from dynamic multimedia. aug: au: Coronel, Jason C Sweitzer, Matthew Bonus, James Alex Dore, Rebecca Lerner, Blue affil: School of Communication, The Ohio State University, Columbus, OH, United States School of Communication, The Ohio State University, Columbus, OH, United StatesMachine Intelligence and Visualization, Sandia National Laboratories, Albuquerque, NM, United States Crane Center for Early Childhood Research and Policy, The Ohio State University, Columbus, OH, United States su: Machine learning Eye tracking Science education Multimedia communications Message processing (Telecommunication) Artificial neural networks sug: subj: Machine learning Eye tracking Science education Multimedia communications Message processing (Telecommunication) Artificial neural networks keyword: copyrightHolder:International Communication Association copyrightYear:2026 eye tracking inLanguage:en machine learning multimedia prediction publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/joc/jqaf036 science learning copyrightHolder:International Communication Association copyrightYear:2026 eye tracking inLanguage:en machine learning multimedia prediction publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/joc/jqaf036 science learning ab: A new era of message processing research will emerge from the convergence of powerful machine learning algorithms with dynamic data from everyday devices equipped with biological sensors. Our study takes critical steps into this era by integrating theory-guided artificial neural networks with eye movements to understand how people learn science concepts from dynamic multimedia. Essential to our theory-guided machine learning approach is a cognitive conceptualization of time as the dynamic interdependence between past and new information that guides how multimedia is attended to and understood. We tracked the eye movements of 197 children as they watched an educational video. We trained two neural network architectures differing in theory guidance to predict learning outcomes using eye movements. The theory-guided architecture, which considered the temporal interdependence of information, yielded more accurate out-of-sample predictions. Our work advances the use of theory-guided machine learning and the development of systems that monitor real-time learning. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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