When coding-and-counting is not enough: using epistemic network analysis (ENA) to analyze verbal data in CSCL research

Andras Csanadi, Brendan Eagan, Ingo Kollar, David Williamson Shaffer, Frank Fischer

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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Resumé

Research on computer-supported collaborative learning (CSCL) is often concerned with the question of how scaffolds or other characteristics of learning may affect learners’ social and cognitive engagement. Such engagement in socio-cognitive activities frequently materializes in discourse. In quantitative analyses of discourse, utterances are typically coded, and differences in the frequency of codes are compared between conditions. However, such traditional coding-and-counting-based strategies neglect the temporal nature of verbal data, and therefore provide limited and potentially misleading information about CSCL activities. Instead, we argue that analyses of the temporal proximity, specifically temporal co-occurrences of codes, provide a more appropriate way to characterize socio-cognitive activities of learning in CSCL settings. We investigate this claim by comparing and contrasting a traditional coding-and-counting analysis with epistemic network analysis (ENA), a discourse analysis technique that models temporal co-occurrences of codes in discourse. We apply both methods to data from a study that compared the effects of individual vs. collaborative problem solving. The results suggest that compared to a traditional coding-and-counting approach, ENA provides more insight into the socio-cognitive learning activities of students.
OriginalsprogEngelsk
TidsskriftInternational Journal of Computer-Supported Collaborative Learning
Vol/bind13
Udgave nummer4
Sider (fra-til)419-438
Antal sider20
ISSN1556-1607
DOI
StatusUdgivet - 1 dec. 2018

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Electric network analysis
network analysis
coding
learning
discourse
Scaffolds
cognitive learning
Students
discourse analysis
neglect
student

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    abstract = "Research on computer-supported collaborative learning (CSCL) is often concerned with the question of how scaffolds or other characteristics of learning may affect learners’ social and cognitive engagement. Such engagement in socio-cognitive activities frequently materializes in discourse. In quantitative analyses of discourse, utterances are typically coded, and differences in the frequency of codes are compared between conditions. However, such traditional coding-and-counting-based strategies neglect the temporal nature of verbal data, and therefore provide limited and potentially misleading information about CSCL activities. Instead, we argue that analyses of the temporal proximity, specifically temporal co-occurrences of codes, provide a more appropriate way to characterize socio-cognitive activities of learning in CSCL settings. We investigate this claim by comparing and contrasting a traditional coding-and-counting analysis with epistemic network analysis (ENA), a discourse analysis technique that models temporal co-occurrences of codes in discourse. We apply both methods to data from a study that compared the effects of individual vs. collaborative problem solving. The results suggest that compared to a traditional coding-and-counting approach, ENA provides more insight into the socio-cognitive learning activities of students.",
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    When coding-and-counting is not enough : using epistemic network analysis (ENA) to analyze verbal data in CSCL research. / Csanadi, Andras; Eagan, Brendan; Kollar, Ingo; Shaffer, David Williamson; Fischer, Frank.

    I: International Journal of Computer-Supported Collaborative Learning, Bind 13, Nr. 4, 01.12.2018, s. 419-438.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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