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Many conversations surrounding improvement of large‐enrollment college science, technology, engineering & mathematics (STEM) courses focus primarily (or solely) on changing instructional practices. By reducing dynamic, complex learning environments to collections of teaching methods, we neglect other meaningful parts of a course ecosystem (e.g., curriculum, assessments). Here, we advocate extending STEM education reform conversations beyond “active versus passive learning.” We argue communities of researchers and instructors would be better served if what we teach and assess was discussed alongside how we teach. To enable nuanced conversations about the characteristics of learning environments that support students in explaining phenomena, we defined a model of college STEM learning environments which attends to the intellectual work emphasized and rewarded on exams (i.e., assessment emphasis), what is taught in whole‐class meetings (i.e., instructional emphasis), and how those meetings are enacted (i.e., instructional practices). We subsequently characterized three distinct chemistry courses and qualitatively examined the characteristics of chemistry learning environments that effectively supported students in explaining why a beaker of water warms as a white solid dissolves. Furthermore, we quantitatively investigated the extent to which measures of incoming preparation explained variance in students’ explanations relative to enrollment in each learning environment. Our findings demonstrate that learning environments that effectively supported learners in explaining dissolution emphasized how and why salts dissolve in‐class and on assessments. Changing teaching methods in an otherwise traditionally structured course (i.e., a course organized by topics that primarily assesses math and recall) did not appear to impact the sophistication of students’ explanations. Additionally, we observed that learning environment enrollment explained substantially more of the variance observed in students’ explanations than measures of precollege math preparation. This finding suggests that emphasizing and rewarding the construction of causal accounts for phenomena in‐class and on assessments may support more equitable achievement.

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.