Since its introduction by Glass in the 1970s, meta-analysis has become a widely accepted and the most preferred approach to conducting research synthesis. Overcoming the weaknesses commonly associated with traditional narrative review and vote counting, meta-analysis is a statistical method of systematically aggregating and analyzing empirical studies by following well-established procedures. The findings of a meta-analysis, when appropriately conducted, are able to inform important policy decisions and provide practical pedagogical suggestions. With the growing number of publications employing meta-analysis across a wide variety of disciplines, it has received criticism due to its inconsistent findings derived from multiple meta-analyses in the same research domain. These inconsistencies have arisen partly due to the alternatives available to meta-analysts in each major meta-analytic procedure. Researchers have therefore recommended transparent reporting on the decision-making for every essential judgment call so that the results across multiple meta-analyses become replicable, consistent, and interpretable. This research explored the degree to which meta-analyses in the computer-assisted language learning (CALL) discipline transparently reported their decisions in every critical step. To achieve this aim, we retrieved 15 eligible meta-analyses in CALL published between 2003 and 2015. Features of these meta-analyses were extracted based on a codebook modified from Cooper (2003) and Aytug, Rothstein, Zhou and Kern (2012). A transparency score of reporting was then calculated to examine the degree to which these meta-analyses are compliant with the norms of reporting as recommended in the literature. We then discuss the strengths and weaknesses of the methodologies and provide suggestions for conducting quality meta-analyses in this domain.

中文翻译：

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2003 年至 2015 年 CALL 荟萃分析报告的透明度

自 1970 年代由 Glass 引入以来，荟萃分析已成为一种被广泛接受和最受欢迎的研究综合方法。元分析克服了通常与传统叙事审查和计票相关的弱点，是一种按照既定程序系统地汇总和分析实证研究的统计方法。如果进行得当，荟萃分析的结果能够为重要的政策决策提供信息并提供实用的教学建议。随着越来越多的出版物使用跨学科的荟萃分析，由于同一研究领域的多个荟萃分析得出的结果不一致，它受到了批评。出现这些不一致的部分原因是在每个主要的元分析程序中元分析员都可以使用替代方案。因此，研究人员建议对每个重要判断调用的决策制定透明报告，以便跨多个荟萃分析的结果变得可复制、一致和可解释。这项研究探讨了计算机辅助语言学习 (CALL) 学科中的元分析在多大程度上透明地报告了他们在每个关键步骤中的决定。为了实现这一目标，我们检索了 2003 年至 2015 年间发表的 CALL 中的 15 个符合条件的荟萃分析。这些荟萃分析的特征是根据 Cooper（2003）和 Aytug、Rothstein、Zhou 和 Kern（2012）修改的密码本提取的。然后计算报告的透明度分数，以检查这些荟萃分析在多大程度上符合文献中推荐的报告规范。然后，我们讨论了这些方法的优缺点，并为在该领域进行质量荟萃分析提供了建议。