Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data.
Abstract: The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration gr...
| Publicado en: | Journal of Computer Assisted Learning Vol. 34; no. 2; pp. 193 - 204 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2018
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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=128312184&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128312184 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Apr2018 vid: 34 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 128312184 128312184 128312184 10.1111/jcal.12232 128312184 ppf: 193 ppct: 11 formats: tig: atl: Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data. aug: au: Prieto, L. P. Sharma, K. Kidzinski, Ł. Rodríguez‐Triana, M. J. Dillenbourg, P. affil: Tallinn University, Estonia sug: subj: Teaching Wearable Sensors Machine Learning Human Eye Movements Accelerometry Data Analytics Models, Statistical Funding Source ab: Abstract: The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time) on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye‐tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time‐independent and time‐aware models, achieving median F1 scores of about 0.7–0.8 on leave‐one‐session‐out k‐fold cross‐validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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