为寻求一种更统一的课堂对话测量方法:对话元素复合构念法

IF 2 3区 教育学 Q2 EDUCATION & EDUCATIONAL RESEARCH
Edith Bouton, Christa S.C. Asterhan
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引用次数: 0

摘要

越来越多的学者对课堂对话的关键特征达成了共识。然而,尽管出现了这种概念上的共识,但在定量研究工作中测量和编码的方式差异很大。为了交流、比较和整合这些丰富的实证研究成果,并进一步完善理论,需要一种更统一的方法来衡量课堂对话。我们选择了7个众所周知的编码框架,并确定了一组9个类似粒子的对话元素(DEs),它们位于不同编码类别的基础上,经常出现在课堂对话中,并且可以在会话回合水平上可靠地编码。然后,我们演示了如何通过标记不同de的共同出现并占七个框架中每个框架中的大多数编码类别来重新创建更大的“复合”对话结构集。这种从对话元素到复合结构的方法(DECCA)可以实现互译器的可靠性,同时保持灵活性和全面性,从而能够使用单一的方法方法对大量研究问题进行定量研究。讨论了对未来研究和理论的启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In pursuit of a more unified method to measuring classroom dialogue: The dialogue elements to compound constructs approach

There is increasing scholarly agreement about the key features of academically productive classroom dialogue. Yet, despite this emerging conceptual consensus, the ways in which it is measured and coded in quantitative research efforts vary significantly. In order to communicate, compare and integrate findings from this rich body of empirical research and to further theory refinement, a more unified approach to measuring classroom dialogue is needed. We selected seven well-known coding frameworks and identified a set of nine particle-like dialogue elements (DEs), that lie at the basis of different coding categories, appear frequently in classroom dialogue, and can be reliably coded at the conversational turn level. We then demonstrate how a much larger set of “compound” dialogue constructs can be recreated post-coding, by flagging co-occurrences of different DEs and accounting for the majority of coding categories in each of the seven frameworks. This Dialogue Elements to Compound Constructs Approach (DECCA) then enables interrater reliability, while simultaneously maintaining the flexibility and comprehensiveness needed to enable quantitative research on a large variety of research questions with a single methodological approach. The implications for future research and theory are discussed.

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来源期刊
Learning Culture and Social Interaction
Learning Culture and Social Interaction EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
4.40
自引率
10.50%
发文量
50
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