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The Networking for Science Advancement (NSA) team collected data from multiple general chemistry courses at nine universities within a broad geographic setting in a majority-minority US state. Data include diagnostic scores on the Math-Up Skills Test (MUST), quantitative literacy/quantitative reasoning (QL/QR) quiz, along with student demographics, and overall course grades. From these data the team determined how automaticity skills in procedural arithmetic and quantitative literacy and reasoning can be used to predict success in lower-division chemistry courses. By expanding this dataset, we extended our investigations to discover what characterizes successful and unsuccessful students in general chemistry, first and second semesters (Chem I and II) categorizing by on- and off- sequence courses. Student characteristics studied include factors such as ethnicity, gender, location of residence, and employment status. In a short amount of required classroom time (approximately 35 minutes is needed for students to complete both assessments and a demographic survey), it is possible to identify students at the start of the semester who will struggle in general chemistry. The MUST is the preferred predictor but using the MUST and QL/QR together enhances predictability.


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Shelton, G. R., Simpson, J., & Mason, D. (2023). Identification of unsuccessful students in general chemistry. African Journal of Chemical Education, 13(2), 137-161.