ACS Exams have long been used by departments to evaluate courses by comparison to national norms. However, some faculty may want to focus on improvement of specific aspects of their course and evaluate their efforts using subsets of questions from an ACS Exam. In other instances, a course may not cover all topics appearing on the associated exam. These and similar scenarios pose a problem for instructors wishing to compare the performance of their students, because normalized comparison information provided by the ACS Examinations Institute (ACS-EI) is generated from overall exam scores. To aid in these assessment efforts, ACS-EI is developing an approach to the generation of norms based on subsets of examination data. One immediate issue that arises is the “missing data” problem, in which complete data for individual exam items is not available because questions were left blank or exam reports submitted by instructors did not include individual responses to each question. In these cases, missing data need to be imputed. In this poster, efforts to explore approaches to generate subset norms and impute missing data for the ACS <i>Inorganic Chemistry </i>and <i>Foundations of Inorganic Chemistry </i>examinations will be reported. This work is supported by the Theodore Ashford Fellowship obtained from the ACS Examinations Institute.

Developing a small batch approach to ACS Examination norms: A case study in inorganic chemistry

253rd National Meeting (2017)

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