As of 1999, high school teachers without majors in their subject areas number 37% of biology teachers, 59% of physical science teachers, and 60% of mathematics teachers. These discouraging statistics grow more extreme in middle schools and high poverty public high schools, especially regarding mathematics and physical sciences instruction. The statistics are especially worrisome given the strong correlation between thorough teacher subject matter preparation and higher student performance. Unfortunately, the literature is limited in terms of a direct comparison between mathematics and science majors and individuals who become mathematics and science teachers. This study was undertaken to add to the body of literature in hopes of informing universities and school districts of the characteristics of individuals who enter the mathematics and science teacher pipeline.
The purpose of this study was to determine whether predictive relationships exist among the independent variables and the dependent variable, and whether certain attributes account for significant differences between mathematics and science degree earners who choose to enter the mathematics and science teacher pipeline and those who show no interest in mathematics and science teaching. This study provided a snapshot of the characteristics of both groups of individuals.
The sample for this investigation came from the Baccalaureate and Beyond Longitudinal Study (B&B: 08/09) cohort of approximately 19,000. B&B:08/09 examined information on students’ educational and work experiences after they completed a bachelor’s degree, with a special emphasis on the experiences of new elementary and secondary teachers. In the present study, the sample consisted of 2,400 individuals majoring in mathematics and science fields including mathematics and science education.
The research design that was used is the analytical cross sectional design. The analytical cross sectional design investigates associations and measures differences between groups. In this study, deep descriptions were used to describe the sample. A logistic regression analysis was used to assess the degree to which the dependent (outcome) variable, teacher pipeline status, is related to the independent (predictor) variables (persistence, academic performance, selected demographics).
