Analyzing and interpreting data is an important science practice that contributes toward the construction of models from data; yet, there is evidence that students may struggle with making meaning of data. The study reported here focused on characterizing students’ approaches to analyzing rate and concentration data in the context of method of initial rates tasks, a type of task used to construct a rate law, which is a mathematical model that relates the reactant concentration to the rate. Here, we present a large-scale analysis (n = 768) of second-semester introductory chemistry students’ responses to three open-ended questions about how to construct rate laws from initial concentration and rate data. Students’ responses were coded based on the level of sophistication in their responses, and latent class analysis was then used to identify groups (i.e. classes) of students with similar response patterns across tasks. Here, we present evidence for a five-class model that included qualitatively distinct and increasingly sophisticated approaches to reasoning about the data. We compared the results from our latent class model to the correctness of students’ answers (i.e. reaction orders) and to a less familiar task, in which students were unable to use the control of variables strategy. The results showed that many students struggled to engage meaningfully with the data when constructing their rate laws. The students’ strategies may provide insight into how to scaffold students’ abilities to analyze data.

中文翻译：

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使用潜在类别分析评估学生从数据构建数学模型的能力†

分析和解释数据是重要的科学实践，有助于从数据构建模型。但是，有证据表明，学生可能难以理解数据的含义。本文报道的研究重点在于表征学生在初始速率任务方法（一种用于构建速率定律的任务类型）的背景下分析速率和浓度数据的方法的特征，这是一种将反应物浓度与速率相关联的数学模型。在这里，我们提出了一个大规模分析（ñ= 768）第二学期的入门化学学生对三个开放性问题的回答，这些问题涉及如何根据初始浓度和速率数据来构建速率定律。根据学生回答的复杂程度对他们的回答进行编码，然后使用潜伏类分析来识别在各个任务中具有相似回答模式的学生群体（即班级）。在这里，我们为五类模型提供了证据，该模型包括了定性上不同且日趋复杂的数据推理方法。我们将潜在班级模型的结果与学生答案的正确性进行了比较（即反应顺序）和不太熟悉的任务，即学生无法使用变量控制策略。结果表明，许多学生在构建费率定律时都难以有效地利用数据。学生的策略可以提供有关如何支持学生分析数据的能力的见解。