This study examined high school chemistry students’ understandings of big ideas—matter and energy, how these understandings are related to macro and submicro representations and fine-grained distinguishing characteristics of students’ explanations. The study was conducted in the context of computer-based models and model-based assessments. Qualitative analysis, descriptive statistics and a stepwise Regression model was run to examine students’ explanations to assessment questions and their relationship to quantitative measures of students’ understandings of big ideas. Students’ explanations revealed consistent level 1 and 2 understandings for each big idea. When we examined the relationship between explanations and students’ understandings of big ideas, the step-wise regression model was statistically significant for matter and energy. At the fine-grained level, students’ explanations revealed distinct clusters—knowledge components that students used to construct descriptive and explanatory explanations for each level of understanding. These findings have implications for effective instructional approaches, targeted enactment of computer-based models and understandings of the range of novice chemistry understanding.

本研究考察了高中化学学生对大概念（物质和能量）的理解，这些理解如何与宏观和亚微观表征以及学生解释的细粒度区分特征相关。该研究是在基于计算机的模型和基于模型的评估的背景下进行的。运行定性分析、描述性统计和逐步回归模型来检查学生对评估问题的解释及其与学生对大思想理解的定量测量的关系。学生的解释揭示了对每个大创意的一致的 1 级和 2 级理解。当我们检查解释与学生对大概念的理解之间的关系时，逐步回归模型对于物质和能量具有统计显着性。在细粒度级别，学生的解释揭示了不同的集群——学生用来为每个理解级别构建描述性和解释性解释的知识组件。这些发现对有效的教学方法、基于计算机的模型的有针对性的制定以及对新手化学理解范围的理解具有影响。