Predictors of Choosing Civil Engineering as a Major Among STEM Students in Saint Mary’s University Senior High School

Choosing an academic major is a crucial step in students’ academic careers, especially for those in their first year of college. Selecting an engineering course is particularly important, as it shapes a student’s academic path and future employment opportunities. This study examines particular predictors, such as socioeconomic status, mathematics performance, parental influence, career prospect perception, analytical and problem-solving skills, and spatial ability, that may influence STEM students at Saint Mary's University Senior High School in their decision to select Civil Engineering as their major in college. This study employed qualitative and quantitative approaches to examine factors influencing STEM students’ preference for Civil Engineering. Conducted at Saint Mary’s University with 61 respondents from the Grade 12 Science, Technology, Engineering, and Mathematics strand, the study used a validated questionnaire. The data were analyzed through descriptive statistics, correlation, regression, and thematic analysis. The results revealed that the respondents’ level of preference for civil engineering is moderate. Moreover, the Pearson coefficient of correlation suggests that, among the six predictors examined, only Analytical and Problem-Solving Skills significantly influence students’ preference for Civil Engineering. Furthermore, based on the regression analysis, socio-economic status, spatial test scores, parental influence, career prospect perception, analytical and problem-solving skills, and math performance do not significantly explain students’ preference for Civil Engineering. Lastly, the thematic analysis reveals that academic issues, particularly those related to mathematics, are more prominent concerns compared to external, career, or financial factors. All in all, results showed a moderate preference, with analytical and problem-solving skills as the only significant predictor, while career prospect perception had a slight negative effect. The regression model explained only 9% of the variance, highlighting other possible factors. With its small, localized sample, the study recommends expanding predictors, diversifying samples, and strengthening career guidance programs.