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.

中文翻译：

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当编码和计数还不够时：在CSCL研究中使用认知网络分析（ENA）分析语言数据

关于计算机支持的协作学习（CSCL）的研究通常涉及以下问题：支架或学习的其他特征如何影响学习者的社交和认知参与度。这种参与社会认知活动的行为经常在话语中实现。在话语的定量分析中，通常对话语进行编码，并比较条件之间的编码频率差异。但是，这种传统的基于编码和计数的策略忽略了时间语言数据的性质，因此提供有关CSCL活动的有限且可能产生误导的信息。取而代之的是，我们认为对时间邻近性（特别是代码的时间共现）的分析提供了一种更合适的方法来表征CSCL环境中学习的社会认知活动。我们通过将传统的编码和计数分析与认知网络分析进行比较和对比来调查此主张（ENA），一种话语分析技术，用于模拟话语中代码的时间共现。我们将两种方法都应用于一项研究中的数据，该研究比较了个人问题解决与协作问题解决的效果。结果表明，与传统的编码和计数方法相比，ENA提供了对学生的社会认知学习活动的更多见解。