Predictors of College Success for STEM Majors
Abstract
The purpose of this quantitative study was to identify the characteristics of students who persisted in a STEM major and examine differences in the aggregate College Persistence Questionnaire (CPQ) scores of enrolled College of Engineering students by enrollment classification (sophomore students compared to senior students), and type of high school attended (STEM high school versus non-STEM high school). The study employed a causal-comparative design using a survey for data collection. The literature review revealed that pre-college characteristics (i.e., type of high school, STEM course-taking, etc.), self-efficacy, and student engagement influenced persistence and retention decisions. Therefore, the study also examined the type of high school enrolled College of Engineering students attended. This research's data were collected using a modified version of the College Persistence Questionnaire (CPQ) (Davidson et al.,2009). The final participants in the study consisted of 56 College of Engineering undergraduate students. The collected data from the CPQ were analyzed using multiple linear regression to evaluate to what extent enrolled undergraduate College of Engineering students' total college persistence scores could be predicted from their student classification and type of high school attended (STEM school versus non-STEM school). Among the study findings, it was concluded that student classification was not a statistically significant predictor of the aggregate CPQ score. However, the type of school was significant in predicting aggregate CPQ scores among enrolled undergraduate College of Engineering students (p < .05), with those who attended STEM high schools reporting, on average, 0.289 units higher in scores than those from non-STEM high schools.
Subject Area
Educational leadership|Higher Education Administration|Higher education|Science education
Recommended Citation
Harry T. Ingle,
"Predictors of College Success for STEM Majors"
(2023).
ETD Collection for Tennessee State University.
Paper AAI30693730.
https://digitalscholarship.tnstate.edu/dissertations/AAI30693730