Time to fill the gaps: a mixed-methods study into the role of the time variable in the construct of computer-administered C-Tests in three languages

填补空白的时间：时间变量在三种语言的计算机管理 C 测试构建中的作用的混合方法研究

• 批准号: 462766474
• 负责人: Professorin Dr. Anastasia Drackert
• 金额: --
• 依托单位: Gesellschaft für Akademische Studienvorbereitung und Testentwicklung e. V. (g.a.s.t.)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/462766474?language=en
• 关键词: Time; fill; gaps; mixed; methods

C-Tests are highly-efficient tools for measuring global language proficiency used for placement and screening purposes, in second language acquisition research (SLA) and educational monitoring (Grotjahn, 2019). In a C-Test, testees are required to fill the gaps in partially deleted words. The new time-reduced speeded C-Test (S-C-Test), appears to be even more promising in terms of efficiency, but has not yet been sufficiently investigated. Grothjahn (2010) argues that, in contrast to a canonical C-Test with a generous time limit that primarily measures the amount of learners’ declarative and procedural knowledge, a S-C-Test additionally gauges the level of automaticity of their skills and the efficiency of information processing. Accordingly, a S-C-Test is expected to correlate higher with listening and speaking skills than a canonical C-Test but lower with writing and reading. Consequently, a partially different construct is hypothesized to underlie a S-C-Test. Although initial findings on the role of time in the construct are available (Fadaeipour & Zohoorian, 2017; Zimmermann, 2019), many questions remain unanswered, especially for computerized C-tests. In particular, we do not know how the time variable influences test reliability and learners’ scores depending on their proficiency level as well as what role different components (e.g., declarative knowledge) play in the construct of differently timed C-Tests. Importantly, no systematic evidence is available on the impact of time on the cognitive processes elicited by the test and realized in the strategies used by learners. Given the crucial role the time variable is hypothesized to play in the construct, the proposed multilingual study aims to specifically investigate this role in a comprehensive way to allow for a higher degree of generalizability of the results for 1) different levels of proficiency, 2) multiple languages (English, German, Russian) and 3) computer-administered C-Tests by employing a variety of methods and collecting a substantial data set building on several relevant types of evidence. Using a sample (n=540) of university language learners, the study aims to provide evidence on what differently timed C-Tests measure, building upon and furthering our knowledge about the fluidity of the construct (Drackert & Timukova, 2020 a&b; Sigott, 2006). Moreover, this will be the first study to look into the componential structure of the C-Test construct using structural equation modeling and process-oriented video-based analysis of strategies. The improved understanding of the influence of time on the C-Test construct and of the construct as such will be beneficial for the practical use of C-Tests. It will allow stakeholders to tailor the instrument to specific purposes and groups and will lead to better informed decisions about learners’ L2 proficiency. Consequently, it will make the tool more precise, more efficient, and more cost-effective.

c - test是衡量全球语言能力的高效工具，用于第二语言习得研究（SLA）和教育监测中的安置和筛选目的（Grotjahn, 2019）。在c测试中，被测试者被要求填补部分被删除的单词的空白。新的缩短速度的C-Test （S-C-Test）在效率方面似乎更有希望，但尚未得到充分的研究。Grothjahn（2010）认为，与标准的C-Test相比，S-C-Test具有宽松的时间限制，主要衡量学习者陈述性和程序性知识的数量，S-C-Test还衡量他们技能的自动化水平和信息处理的效率。因此，与标准的C-Test相比，s - c测试与听力和口语技能的相关性更高，但与写作和阅读的相关性较低。因此，一个部分不同的结构被假设为s - c检验的基础。虽然有关于时间在构建中的作用的初步发现（Fadaeipour & Zohoorian, 2017; Zimmermann, 2019），但许多问题仍未得到解答，特别是对于计算机c测试。特别是，我们不知道时间变量如何影响测试信度和学习者的分数，取决于他们的熟练程度，以及不同成分（例如，陈述性知识）在不同时间的c - test的结构中起什么作用。重要的是，没有系统的证据表明时间对学习者所使用的策略所引发的认知过程的影响。考虑到假设时间变量在构建中发挥的关键作用，拟议的多语言研究旨在以全面的方式专门调查这一作用，以便通过采用各种方法并收集基于几种相关证据类型的大量数据集，对1)不同水平的熟练程度，2)多种语言（英语，德语，俄语）和3)计算机管理的c测试的结果进行更高程度的推广。使用大学语言学习者的样本（n=540），该研究旨在提供证据，证明不同时间的c测试测量，建立并进一步了解结构的流动性（Drackert & Timukova, 2020; Sigott, 2006）。此外，这将是第一个使用结构方程模型和基于过程的视频策略分析来研究c测试结构的成分结构的研究。更好地理解时间对C-Test结构和结构本身的影响，将有利于C-Test的实际使用。它将允许利益相关者根据特定的目的和群体定制工具，并将对学习者的第二语言熟练程度做出更明智的决定。因此，它将使工具更精确，更高效，更具成本效益。