First-semester general chemistry is a known “gatekeeper” course due to its high failure rate. These higher education courses are taken by students who for the most part are regularly admitted freshmen, yet many struggle to succeed. In this investigation researchers from six higher-education institutions of varied sizes with student bodies of different ethnic composition teamed up to investigate the Math-Up Skills Test (MUST) as a potential tool to identify at-risk students in first-semester general chemistry (Chem I). A large population (N = 1073) was given the MUST at the beginning of the semester. The MUST had good internal consistency with the sample (Cronbach's alpha = 0.856). The MUST offers a quick 15 minute assessment of basic mathematics ability without a calculator. Instructors may find it easier than other documented predictors, which may take more time or involve obtaining student records (e.g., Math SAT). Results from the MUST support prior findings that mathematics skills correlate with course grades in chemistry. Poor arithmetic automaticity may be an underlying factor in lower performance by many students. With statistical modeling, the MUST, along with other demographic variables, was able to identify students who would go on to make a 69.5% or better in Chem I with a high percent of accuracy. The MUST, in tandem with other demographic variables, should be considered a potential tool for early identification of students at-risk for failing the class.

Completion of a first-semester chemistry (Chem I) course lays the foundation for understanding second-semester chemistry (Chem II) topics. The purpose of this study is to evaluate the influence of basic arithmetic skills on students’ Chem II success in understanding mathematics-grounded concepts (e.g., solutions and aqueous reactions, kinetics, equilibrium, acids and bases, solubility and equilibria, thermodynamics, electrochemistry, and nuclear chemistry). Previous studies suggest a strong correlation between arithmetic preparation and performance in general chemistry courses, though few have focused on the second-semester course. In this investigation, researchers from six higher-education institutions in Texas, USA of different sizes and with student bodies of different diversities have collaborated to determine whether the Math-Up Skills Test (MUST) is able to reliably identify at-risk students from a population of n = 1599 at the beginning of a Chem II course.

This contribution reports the process and outcomes of a multiyear effort that supported institutions in reforming general chemistry using multidimensional learning approaches in the form of performance expectations. Performance expectations are based on evidence-centered assessment design principles and describe what learners should be able to do with their knowledge, a subtle (but profound) shift from traditional course learning goals. The effort grew from the recommendations of a task force appointed in 2015 by the American Chemical Society's Division of Chemical Education and Society Committee on Education. This task force recommended a participatory process for creating general chemistry performance expectations that was distributed over, and also coordinated across, multiple institutions. With support from the ACS Education Division, this recommendation was enacted through workshops which supported faculty in developing activities and assessments that integrated content, science and engineering practices, and cross-cutting conceptsa three-dimensional structure based on the National Research Council report A Framework for K-12 Education. From these workshops, a group of faculty committed to implementing three-dimensional performance expectations in their courses evolved. In practice, these faculty found that their institutional work resulted in designing learning performances that, while also three-dimensional, were of narrower content focus than is typical of performance expectations. This development process also led faculty to use the structures of evidence-centered design and multidimensional learning to document learning activity designs in new ways, generating a consensus activity structure. As examples of how faculty used this consensus activity structure as a new way to examine student learning and performances, development artifacts from four of the participating institutions is presented.