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Before and during COVID-19: A Cohesion Network Analysis of Students' Online Participation in Moodle Courses

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Title: Before and during COVID-19: A Cohesion Network Analysis of Students' Online Participation in Moodle Courses
Language: English
Authors: Dascalu, Maria-Dorinela, Ruseti, Stefan, Dascalu, Mihai, McNamara, Danielle S., Carabas, Mihai, Rebedea, Traian
Source: Grantee Submission. 2021 121.
Peer Reviewed: Y
Page Count: 19
Publication Date: 2021
Sponsoring Agency: Institute of Education Sciences (ED)
Office of Naval Research (ONR) (DOD)
Contract Number: R305A180261
R305A180144
N000141712300
N000141912424
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: COVID-19, Pandemics, Integrated Learning Systems, School Closing, Educational Technology, Technology Uses in Education, Student Participation, Student Behavior, Interaction, Undergraduate Students, Artificial Intelligence, Foreign Countries, Natural Language Processing, Grades (Scholastic), Visual Aids
Geographic Terms: Romania
DOI: 10.1016/j.chb.2021.106780
ISSN: 0747-5632
Abstract: The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students' behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018-2019 when lower fluctuations in participation were observed. The prediction model for the 2018-2019 academic year is partially generalizable to the second year, but explains a considerably lower variance (R[subscript 2] = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2021
Accession Number: ED616067
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Before and during COVID-19: A Cohesion Network Analysis of Students' Online Participation in Moodle Courses
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2021 121.
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  Data: 19
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Romania%22">Romania</searchLink>
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  Data: 10.1016/j.chb.2021.106780
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  Data: 0747-5632
– Name: Abstract
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  Data: The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students' behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018-2019 when lower fluctuations in participation were observed. The prediction model for the 2018-2019 academic year is partially generalizable to the second year, but explains a considerably lower variance (R[subscript 2] = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic.
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        Value: 10.1016/j.chb.2021.106780
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      – Text: English
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      Pagination:
        PageCount: 19
    Subjects:
      – SubjectFull: COVID-19
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      – SubjectFull: Pandemics
        Type: general
      – SubjectFull: Integrated Learning Systems
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      – SubjectFull: School Closing
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      – SubjectFull: Visual Aids
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      – SubjectFull: Romania
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      – TitleFull: Before and during COVID-19: A Cohesion Network Analysis of Students' Online Participation in Moodle Courses
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