Date of Award
Dissertation - NSU Access Only
Doctor of Philosophy in Education
Abraham S. Fischler College of Education
Steven A. Hecht
Maria R. Ligas
Dana S. Mills
Chemistry Achievement, Chemistry Enrollment, Logistic Regression, Predictive Modeling, Education, Science education, Secondary education
The aim of this study was to identify predictors of student enrollment and successful achievement in 10th grade chemistry courses for a sample drawn from a single academic cohort from a single metropolitan school district in Florida. Predictors included, among others, letter grades for courses completed in academic classes for each independent grade level, sixth through 10th grade, as well as standardized test scores on the Florida Comprehensive Assessment Test and demographic variables. The predictive models demonstrated that it is possible to identify student attributes that result in either increased or decreased odds of enrollment in chemistry courses. The logistic models identified subsets of students who could potentially be candidates for academic interventions, which may increase the likelihood of enrollment and successful achievement in a 10th grade chemistry course. Predictors in this study included grades achieved for each school year for coursework completed in mathematics, English, history, and science, as well as reported FCAT performance band scores for students from sixth through 10th grade.
Demographics, socioeconomic status, special learning services, attendance rates, and number of suspensions are considered. The results demonstrated that female students were more likely to enroll in and pass a chemistry course than their male peers. The results also demonstrated that prior science achievement (followed closely by mathematics achievement) was the strongest predictor of enrollment in—and passing of—a chemistry course. Additional analysis also demonstrated the relative stability of academic GPA per discipline from year to year; cumulative achievement was the best overall indicator of course enrollment and achievement.
Nathan Lee Charnock. 2016. Predictive Modeling of Enrollment and Academic Success in Secondary Chemistry. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, Abraham S. Fischler College of Education. (36)