Predictors of Advanced Placement Chemistry Performance: Teacher and School-Level Academic and Demographic Characteristics
Year: 2026
Type: article
Study design: Observational Study
Abstract: High Resolution Image Download MS PowerPoint Slide Advanced Placement (AP) science participation and performance have been identified as predictors of high school achievement; postsecondary academic performance; science, technology, engineering, and mathematics (STEM) persistence; and degree completion. This study examined student, teacher, and school-level predictors of AP Chemistry performance among a sample of 148 teachers and 2,991 students in New York State. The conceptual framework is derived from theories suggesting that teacher variables including preparation and experience, as well as school-level contextual variables including curricular offerings and demographics, are predictive of student performance. A multiple linear regression model demonstrated that the percentages of test-takers who were women, underrepresented minorities in STEM, and economically disadvantaged were negative predictors of AP Chemistry performance. Chemistry teacher characteristics, including certification type, years of experience, gender, and course load in chemistry, were not predictors of student performance. Additionally, AP Chemistry enrollment and access to other AP science courses within the school district did not predict student performance. These results suggest that student demographic characteristics largely predicted performance, while minimum teacher preparation in undergraduate chemistry coursework (18 vs 30 credits) did not. This state-level study highlights the need for more comprehensive, multistate data to identify targeted interventions to improve AP Chemistry performance, particularly among traditionally underserved students.
Source: Journal of Chemical Education
Institutions: Stony Brook University, Farmingdale State College
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Funder: State University of New York
OpenAlex ID: W7148726474
