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Revealing Impact Factors on Student Engagement: Learning Analytics Adoption in Online and Blended Courses in Higher Education

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Title: Revealing Impact Factors on Student Engagement: Learning Analytics Adoption in Online and Blended Courses in Higher Education
Authors: Fan, Si (ORCID 0000-0003-1572-3677), Chen, Lihua, Nair, Manoj (ORCID 0000-0002-6424-1677), Garg, Saurabh, Yeom, Soonja (ORCID 0000-0002-5843-101X), Kregor, Gerry (ORCID 0000-0003-2569-6326), Yang, Yu (ORCID 0000-0001-9354-3909), Wang, Yanjun
Source: Education Sciences. 2021 11.
Availability: MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: education@mdpi.com; e-mail: indexing@mdpi.com; Web site: https://www.mdpi.com/journal/education
Peer Reviewed: Y
Page Count: 17
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Learner Engagement, Learning Analytics, Integrated Learning Systems, Adoption (Ideas), Online Courses, Blended Learning, Discussion Groups, Course Content, Behavior Patterns, Teacher Participation, Foreign Countries, College Students
Geographic Terms: Australia
ISSN: 2227-7102
Abstract: This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framework adapted from Community of Inquiry (CoI) framework by Garrison et al. (2000), the data were segregated into three groups for analysis: Student Engagement, Course Content and Teacher Input. The data analysis revealed a positive correlation between Student Engagement and Teacher Input, and interestingly, a negative correlation between Student Engagement and Course Content when a certain threshold was exceeded. The findings of the study offer useful suggestions for future course design, and pedagogical approaches teachers can adopt to foster student engagement.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1317965
Database: ERIC
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  Data: Revealing Impact Factors on Student Engagement: Learning Analytics Adoption in Online and Blended Courses in Higher Education
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  Data: <searchLink fieldCode="AR" term="%22Fan%2C+Si%22">Fan, Si</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1572-3677">0000-0003-1572-3677</externalLink>)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Lihua%22">Chen, Lihua</searchLink><br /><searchLink fieldCode="AR" term="%22Nair%2C+Manoj%22">Nair, Manoj</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6424-1677">0000-0002-6424-1677</externalLink>)<br /><searchLink fieldCode="AR" term="%22Garg%2C+Saurabh%22">Garg, Saurabh</searchLink><br /><searchLink fieldCode="AR" term="%22Yeom%2C+Soonja%22">Yeom, Soonja</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5843-101X">0000-0002-5843-101X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kregor%2C+Gerry%22">Kregor, Gerry</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2569-6326">0000-0003-2569-6326</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yu%22">Yang, Yu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9354-3909">0000-0001-9354-3909</externalLink>)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yanjun%22">Wang, Yanjun</searchLink>
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  Data: MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: education@mdpi.com; e-mail: indexing@mdpi.com; Web site: https://www.mdpi.com/journal/education
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  Data: 17
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+Learning+Systems%22">Integrated Learning Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Adoption+%28Ideas%29%22">Adoption (Ideas)</searchLink><br /><searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Blended+Learning%22">Blended Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Discussion+Groups%22">Discussion Groups</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Content%22">Course Content</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Patterns%22">Behavior Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Participation%22">Teacher Participation</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Australia%22">Australia</searchLink>
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  Data: 2227-7102
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  Label: Abstract
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  Data: This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framework adapted from Community of Inquiry (CoI) framework by Garrison et al. (2000), the data were segregated into three groups for analysis: Student Engagement, Course Content and Teacher Input. The data analysis revealed a positive correlation between Student Engagement and Teacher Input, and interestingly, a negative correlation between Student Engagement and Course Content when a certain threshold was exceeded. The findings of the study offer useful suggestions for future course design, and pedagogical approaches teachers can adopt to foster student engagement.
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  Data: 2021
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  Data: EJ1317965
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      Pagination:
        PageCount: 17
    Subjects:
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Integrated Learning Systems
        Type: general
      – SubjectFull: Adoption (Ideas)
        Type: general
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: Blended Learning
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      – SubjectFull: Discussion Groups
        Type: general
      – SubjectFull: Course Content
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      – SubjectFull: Behavior Patterns
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      – SubjectFull: Teacher Participation
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      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: College Students
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      – SubjectFull: Australia
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      – TitleFull: Revealing Impact Factors on Student Engagement: Learning Analytics Adoption in Online and Blended Courses in Higher Education
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              Y: 2021
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