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Using Learning Analytics to Understand K-12 Learner Behavior in Online Video-Based Learning

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Title: Using Learning Analytics to Understand K-12 Learner Behavior in Online Video-Based Learning
Language: English
Authors: Eamon Vale (ORCID 0000-0002-1033-8318), Garry Falloon (ORCID 0000-0002-6369-8771)
Source: Online Learning. 2024 28(1):44-68.
Availability: Online Learning Consortium, Inc. P.O. Box 1238, Newburyport, MA 01950. Tel: 888-898-6209; Fax: 888-898-6209; e-mail: olj@onlinelearning-c.org; Web site: https://olj.onlinelearningconsortium.org/index.php/olj/index
Peer Reviewed: Y
Page Count: 25
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Elementary Education
Secondary Education
Descriptors: Learning Analytics, Elementary School Students, Secondary School Teachers, Electronic Learning, Video Technology, Learner Engagement, Teaching Methods, Foreign Countries
Geographic Terms: Australia
ISSN: 2472-5749
2472-5730
Abstract: This research investigated the potential of learning analytics (LA) as a tool for identifying and evaluating K-12 student behaviors associated with active learning when using video learning objects within an online learning environment (OLE). The study focused on the application of LA for evaluating K-12 student engagement in videobased learning--an area of inquiry highlighted in literature as important but significantly under-researched. Results determined that the LA method could identify active-learning behaviors and that LA can play a valuable role in providing information on learner activity in autonomous K-12 OLEs. However, LA did not provide a complete picture of learner behavior and viewing strategies, highlighting the importance of a multi-method approach to research on K-12 online learner behaviors. It is anticipated the accessible approach outlined in this study will provide educators with a viable means of using LA techniques to better understand how learners interact with course content and learning objects, greatly assisting the design of online learning programs.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1417958
Database: ERIC
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  Data: Using Learning Analytics to Understand K-12 Learner Behavior in Online Video-Based Learning
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  Data: <searchLink fieldCode="AR" term="%22Eamon+Vale%22">Eamon Vale</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1033-8318">0000-0002-1033-8318</externalLink>)<br /><searchLink fieldCode="AR" term="%22Garry+Falloon%22">Garry Falloon</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6369-8771">0000-0002-6369-8771</externalLink>)
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  Data: Online Learning Consortium, Inc. P.O. Box 1238, Newburyport, MA 01950. Tel: 888-898-6209; Fax: 888-898-6209; e-mail: olj@onlinelearning-c.org; Web site: https://olj.onlinelearningconsortium.org/index.php/olj/index
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  Data: 2472-5749<br />2472-5730
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  Data: This research investigated the potential of learning analytics (LA) as a tool for identifying and evaluating K-12 student behaviors associated with active learning when using video learning objects within an online learning environment (OLE). The study focused on the application of LA for evaluating K-12 student engagement in videobased learning--an area of inquiry highlighted in literature as important but significantly under-researched. Results determined that the LA method could identify active-learning behaviors and that LA can play a valuable role in providing information on learner activity in autonomous K-12 OLEs. However, LA did not provide a complete picture of learner behavior and viewing strategies, highlighting the importance of a multi-method approach to research on K-12 online learner behaviors. It is anticipated the accessible approach outlined in this study will provide educators with a viable means of using LA techniques to better understand how learners interact with course content and learning objects, greatly assisting the design of online learning programs.
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      – Text: English
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        PageCount: 25
        StartPage: 44
    Subjects:
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Secondary School Teachers
        Type: general
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Video Technology
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Foreign Countries
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      – SubjectFull: Australia
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      – TitleFull: Using Learning Analytics to Understand K-12 Learner Behavior in Online Video-Based Learning
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            NameFull: Garry Falloon
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