– Lower levels of mathematics anxiety, higher levels of mathematics courses completed in high school, positive teacher experiences, and multiple instances of exposure to STEM fields while in middle and high school increased the likelihood that students would choose a STEM major.
– Lower levels of mathematics anxiety and being placed into higher-ability mathematics courses in middle and high school correlated with higher levels of mathematics self-efficacy.
– Higher levels of mathematics self-efficacy in middle and high school led to increased instances of pursuing a STEM career.
– Students enrolled in at least Calculus I while in high school were significantly more likely to choose a STEM major in college.
– Interviews revealed a larger percentage of STEM majors indicating positive mathematics teacher experiences than non-STEM majors.
Persistence and Performance for Latino Community College Students in STEM Majors
1) Do coaching intervention models in STEM courses contribute to student semester-to-semester persistence for Latino community college students who participate in these courses, when compared to students who don’t participate? 2) Do Latino community college students who participate in college STEM courses with coaching intervention models perform better, as measured by final course GPA, when compared to students who do not participate?
A Method for Identifying Variables for Predicting STEM Enrollment
This research examines demographic, academic, attitudinal, andexperiential data from the Cooperative Institutional Research Program (CIRP) for over 12,000 students at two universities to test a methodology for identifying variables showing significant differences between students intending to major in science, technology, engineering, or mathematics (STEM) versus non-STEM subjects. Identifying potential candidates for STEM enrollment necessi-tates a methodology for analyzing databases containing demo-graphic, academic performance, and attitudinal information acrossa wide array of students. Finding variables that are consistently significant predictors of STEM interest and capability across a range of population subgroups requires the ability to examine a large set of variables since some variables may be significant only for specific subgroups.