Unfavorable test-taking behaviors, such as speededness and disengagement, have long been a validity concern for large-scale low-stakes assessments. Understanding the presence and extent of such behaviors is important for ensuring the validity of inferences based on test scores. This study examined test-taking behaviors using item response time (RT), a process data-derived variable from the TIMSS 2019 database. Analyses compared the United States to three other countries (England, Singapore, and the United Arab Emirates) that administered the digital version of TIMSS (eTIMSS) 2019 in English at grade 8. Test-taking behaviors were identified within each country and compared within and across countries. Specifically, to identify distinct types of test-taking behaviors, mixture modeling was employed on RT and item scores from Booklet 1, Part 1, of the eTIMSS 2019 eighth-grade assessment. The results indicated that each country had several latent classes of students with different pacing trajectories and performance. The test-taking behaviors of these latent classes were labeled as Steady; Disengaged or Very disengaged; Speeded or Very speeded; and Efficient and high-performing. Most of the students in each country had a Steady pace (medium to high sum score; steady RT throughout the test): 71% in England, 74% in both Singapore and the United Arab Emirates, and 84% in the United States. Disengaged or Very disengaged students (low sum score; short RT) were identified in each country but were more prevalent in England and the United Arab Emirates (above 20% in both) than in the United States and Singapore (both below 10%). The study also revealed small percentages of Speeded or Very speeded students (low to medium sum score; long RT at first but very short RT toward the end) in England, the United Arab Emirates, and the United States (1%, 5%, and 6%, respectively) but not in Singapore. A unique class of Efficient and high-performing students (high sum score; short RT) was identified only in Singapore (24%). This study demonstrated that mixture modeling is a useful technique for identifying distinct test-taking behaviors and highlighted the presence and extent of unfavorable test-taking behaviors within each selected country using data from Booklet 1, Part 1, of the eTIMSS 2019 eighth-grade assessment.

中文翻译：

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2019 年 eTIMSS 选定国家/地区学生应试行为的识别和跨国比较

不利的应试行为，例如过快和脱离参与，长期以来一直是大规模低风险评估的有效性问题。了解此类行为的存在和程度对于确保基于测试分数的推论的有效性非常重要。本研究使用项目响应时间 (RT) 来检查应试行为，RT 是来自 TIMSS 2019 数据库的过程数据派生变量。分析将美国与其他三个国家（英格兰、新加坡和阿拉伯联合酋长国）进行了比较，这三个国家在 8 年级以英语管理 2019 年 TIMSS 数字版 (eTIMSS)。各国。具体来说，为了识别不同类型的考试行为，对第 1 册第 1 部分中的 RT 和项目分数采用了混合模型，eTIMSS 2019 年八年级评估。结果表明，每个国家都有几个潜在的学生班级，他们的节奏轨迹和表现各不相同。这些潜在类别的考试行为被标记为稳定；不投入或非常不投入；超速或超速；高效、高性能。每个国家的大多数学生都具有稳定的节奏（中到高总分；整个测试过程中 RT 稳定）：英国为 71%，新加坡和阿拉伯联合酋长国为 74%，美国为 84%。每个国家都发现了注意力不集中或非常注意力不集中的学生（总分低；RT 短），但在英国和阿拉伯联合酋长国（均超过 20%）比美国和新加坡（均低于 10%）更为普遍。该研究还显示，在英格兰、阿拉伯联合酋长国和美国，极速学生（低到中等总分；一开始的 RT 长，但到最后的 RT 很短）的比例很小（1%、5%、和 6%），但在新加坡则不然。仅在新加坡（24％）发现了一个独特的高效和高绩效学生类别（高总分；短 RT）。这项研究证明，混合建模是一种识别不同考试行为的有用技术，并使用 eTIMSS 2019 年八年级评估第 1 册第 1 部分的数据强调了每个选定国家/地区中不利考试行为的存在和程度。分别）但不在新加坡。仅在新加坡（24％）发现了一个独特的高效和高绩效学生类别（高总分；短 RT）。这项研究证明，混合建模是一种识别不同考试行为的有用技术，并使用 eTIMSS 2019 年八年级评估第 1 册第 1 部分的数据强调了每个选定国家/地区中不利考试行为的存在和程度。分别）但不在新加坡。仅在新加坡（24％）发现了一个独特的高效和高绩效学生类别（高总分；短 RT）。这项研究证明，混合建模是一种识别不同考试行为的有用技术，并使用 eTIMSS 2019 年八年级评估第 1 册第 1 部分的数据强调了每个选定国家/地区中不利考试行为的存在和程度。