Naumann Johannes
Institute of Educational Research, University of Wuppertal, Wuppertal, Germany.
Front Psychol. 2019 Jun 27;10:1429. doi: 10.3389/fpsyg.2019.01429. eCollection 2019.
In large scale low stakes assessments, students usually choose their own speed at which to work on tasks. At the same time, previous research has shown that in hard tasks, the time students invest is a positive predictor of task performance. From this perspective, a relevant question is whether student dispositions other than the targeted skill might affect students' time on task behavior, thus potentially affecting their task performance and in turn their estimated skill in the target domain. Using PISA 2009 computer based assessment data, the present research investigated for the domain of reading digital text whether three variables that can be assumed to predict performance in digital reading tasks, comprehension skill, enjoyment of reading, and knowledge of reading strategies would also predict how much time students would devote to digital reading tasks, and in particular, whether they would adapt time on task to task difficulty. To address this question, two linear mixed models were estimated that predicted the time students spent on a task, and the average time students spent on relevant pages within each task, by the interaction of task difficulty with comprehension skill, enjoyment of reading, and knowledge of reading strategies. To account for time on task being nested in students and tasks, random effects for persons and tasks were included. The interaction of task difficulty with gender and Socio-Economic Status (SES) was included for control purposes. Models were estimated individually for 19 countries, and results integrated meta-analytically. In line with predictions, for both time on task indicators, significant positive interactions were found with comprehension skill, enjoyment of reading, and knowledge of reading strategies. These interactions indicated that in students with high comprehension skill, enjoyment of reading, and knowledge of reading strategies there was a stronger association of task difficulty with time on task than in students low in either of these variables. Thus, skilled comprehenders, students enjoying reading, and students in command of reading strategies behaved more adaptively than lower skilled, motivated, or knowledgeable students. Implications of these findings for the validity of self-paced computer-based assessments are discussed.
在大规模低风险评估中，学生通常自行选择完成任务的速度。与此同时，先前的研究表明，在困难任务中，学生投入的时间是任务表现的一个积极预测指标。从这个角度来看，一个相关的问题是，除了目标技能之外，学生的其他特质是否会影响他们在任务上花费的时间行为，从而可能影响他们的任务表现，进而影响他们在目标领域的估计技能。本研究利用2009年国际学生评估项目（PISA）基于计算机的评估数据，针对阅读数字文本领域，调查了三个可被认为能预测数字阅读任务表现的变量，即理解能力、阅读乐趣和阅读策略知识，是否也能预测学生在数字阅读任务上会花费多少时间，特别是他们是否会根据任务难度调整任务时间。为了解决这个问题，估计了两个线性混合模型，通过任务难度与理解能力、阅读乐趣和阅读策略知识的相互作用，预测学生在一项任务上花费的时间，以及学生在每项任务中相关页面上花费的平均时间。为了考虑任务时间嵌套在学生和任务之中的情况，纳入了个体和任务的随机效应。为了进行控制，纳入了任务难度与性别和社会经济地位（SES）的相互作用。针对19个国家分别估计了模型，并对结果进行了元分析整合。与预测一致，对于两个任务时间指标，都发现了与理解能力、阅读乐趣和阅读策略知识的显著正相关。这些相互作用表明，在理解能力强、阅读乐趣高和阅读策略知识丰富的学生中，任务难度与任务时间的关联比在这些变量较低的学生中更强。因此，理解能力强的学生、喜欢阅读的学生和掌握阅读策略的学生比技能较低、积极性不高或知识较少的学生表现得更具适应性。讨论了这些发现对自定进度的基于计算机的评估有效性的影响。
