Background Concept inventories (CIs) are commonly used in engineering disciplines to assess students' conceptual understanding and to evaluate instruction, but educators often use CIs without sufficient evidence that a structured approach has been applied to validate inferences about student thinking. Purpose We propose an analytic framework for evaluating the validity arguments of CIs. We focus on three types of claims: that CI scores enable one to infer (1) students' overall understanding of all concepts identified in the CI, (2) students' understanding of specific concepts, and (3) students' propensity for misconceptions or common errors. Method We applied our analytic framework to three CIs: the Concept Assessment Tool for Statics (CATS), the Statistics Concept Inventory (SCI), and the Dynamics Concept Inventory (DCI). Results Using our analytic framework, we found varying degrees of support for each type of claim. CATS and DCI analyses indicated that the CIs could reliably measure students' overall understanding of all concepts identified in the CI, whereas SCI analyses provided limited evidence for this claim. Analyses revealed that the CATS could accurately measure students' understanding of specific concepts; analyses for the other two CIs did not support this claim. None of the CI analyses provided evidence that the instruments could reliably measure students' misconceptions and common errors. Conclusions Our analytic framework provides a structure for evaluating CI validity. Engineering educators can apply this framework to evaluate aspects of CI validity and make more warranted uses and interpretations of CI outcome scores.

An understanding of protein folding relies on a solid foundation of a number of critical chemical concepts, such as molecular structure, intra-/intermolecular interactions, and relating structure to function. Recent reports show that students struggle on all levels to achieve these understandings and use them in meaningful ways. Further, several reports show that the visualization techniques employed to help students understand protein structure often lead to confusion and propagate further misconceptions. Here, we report on a lab exercise using computer-based modeling to support student proficiency in using and making models and understanding H-bonding and the hydrophobic effect in the context of protein folding. We analyzed student drawings and explanations of protein structure and found significant improvements from pre- to postlab, indicating that students improved their understanding of protein folding. Further, we report on how we systematically refined our laboratory materials based on student work.

Introductory Chemistry laboratories must go beyond "cookbook" methods to illustrate how chemistry concepts apply to complex, real-world problems. In our case, we are preparing students to use their chemistry knowledge in the healthcare profession. The experiment described here explicitly models three important chemical concepts: dialysis of small molecules (dye), reversible binding (dye binding to albumin), and competitive binding (dye and a competitor binding to albumin). Moreover, each concept is intimately related to a physiological phenomenon: dialysis is used to treat renal failure, drugs travel in the blood bound to albumin, and competitive albumin binding is a common drug− drug interaction. In the context of this simple series of experiments, students create models, use evidence to validate their models, and finally use their understanding to describe physiological phenomena. This laboratory experiment was implemented in a 100level course for predominantly prenursing majors. Student pre-and postlab models were examined, illustrating an improved conceptual understanding upon performing the lab and use of evidence to improve or support models. This experiment can be performed in 1 h, and can be adapted as a lecture demonstration.

We implemented a laboratory curriculum reform to teach foundational concepts in chemistry, particularly those concepts related to healthcare, in a chemistry course for prenursing students. Here, we discuss the reform, exploring how students built upon understandings gained in lab and correlating lab learning to course outcomes. We further discuss shifts in student work as they move through the course. As the course progressed, students became familiar with the pedagogy but also faced more challenging tasks. We present details on several of the laboratories that build the groundwork for understanding chemical principles, including the following: intermolecular forces, physical properties, acid–base chemistry, equilibrium, and chemical reactions. We further share our observations of student interactions around in-lab prompts and activities, and how these interactions inform our teaching. Our reform aims to improve critical thinking skills, namely, making and using models, observation skills, reasoning with evidence, and applying concepts to new problems. The laboratory procedures presented here modify those commonly found in the chemistry curriculum with a consistent student-centered pedagogy. We hope the simplicity and popularity of the lab procedures will allow for broad implementation of Rickey’s MORE (Model, Observe, Reflect, Explain) pedagogy, and we hope our lessons in implementation will broadly benefit those who are implementing new lab curricula.

Acid–base chemistry is an important and challenging topic for virtually all chemistry students. Recent works have focused on conceptually linking molecular and symbolic representations to experimental evidence for many chemical concepts. In this work we describe a laboratory exercise designed to address students’ conceptual challenges regarding acid–base chemistry, particularly with respect to organic acids, titrations, and pK a’s. This work is part of a series of laboratory exercises developed for prehealth students using the MORE (model, observe, reflect, explain) pedagogy. In this lab, students titrated an amino acid with sodium hydroxide according to lab procedures typically used in a prehealth lab. Our work differs from traditional laboratories in the emphasis on connecting molecular-level representations to symbolic representations and experimentally derived evidence. Using this course environment, we sought to understand the ways in which students’ understandings (molecular-level, symbolic, or evidential) are coconstructed or discrete. We further sought to guide students toward connecting these understandings and report on our successes and areas for improvement.