Understanding the nature and purpose of models, including mathematical models, is critical to enabling undergraduate chemistry students to use models to predict and explain phenomena. However, students often do not have systematic conceptions about different kinds of models. To gain a sense of how students understand different models in the general chemistry curriculum, we developed a survey to examine students’ reasoning about models generally and in some specific contexts within the general chemistry curriculum. The findings suggest that students have some productive ideas about what kinds of representations are scientific models and the characteristics of those models; however, students may not recognize these characteristics in models which are mathematical or graphical in nature.

Developing and using scientific models is an important scientific practice for science students. Undergraduate chemistry curricula are often centered on established disciplinary models, and assessments typically provide students with opportunities to use these models to predict and explain chemical phenomena. However, traditional curricula generally provide few opportunities for students to consider the epistemic nature of models and the process of modeling. To gain a sense of how introductory chemistry students understand model changeability, model multiplicity, the evaluation of models, and the process of modeling, we use a construct‐mapping approach to characterize the sophistication of students' epistemic knowledge of models and modeling. We present a set of four related construct maps that we developed based on the work of other scholars and empirically validated in an undergraduate introductory chemistry setting. We use the construct maps to identify themes in students' responses to an open‐ended survey instrument, the models in chemistry survey, and discuss the implications for teaching.

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.