From signals to knowledge: A conceptual model for multimodal learning analytics.
Abstract: Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in...
| Published in: | Journal of Computer Assisted Learning Vol. 34; no. 4; pp. 338 - 350 |
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| Main Authors: | , , , |
| Format: | equations & formulas review tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Aug2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130898740&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130898740 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: 130898740 130898740 130898740 10.1111/jcal.12288 130898740 ppf: 338 ppct: 12 formats: tig: atl: From signals to knowledge: A conceptual model for multimodal learning analytics. aug: au: Di Mitri, Daniele Schneider, Jan Specht, Marcus Drachsler, Hendrik affil: Welten Institute ‐ Research Centre for Learning, Teaching and Technology, Open University of Netherlands, The Netherlands sug: subj: Learning Methods Conceptual Framework Data Analytics Machine Learning Learning Theory ab: Abstract: Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human‐driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science. pubtype: Academic Journal doctype: equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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