2023 ASEE Annual Conference & Exposition

Improving the overall progress students make toward desired educational outcomes will result in graduating competent workforce that can function effectively in advancing the scientific and technological landscape of the nation. In the past, various studies have investigated what factors influence the academic achievement of undergraduate students in general. However, fewer studies have been conducted to investigate how these factors influence the progress toward desired educational outcomes of high-achieving engineering students in particular. In the present study, predictive modeling of high-achieving engineering students’ achievement in terms of the overall progress they make toward desired educational outcomes is conducted.

Fifty-one high-achieving engineering students at a university located in the Mountain West region of the United States participated in this study. The College Students’ Experience Questionnaire (CSEQ) and Learning and Study Strategies Inventory (LASSI) were instruments used for data collection. Data analysis conducted includes a normality test, descriptive analysis, correlational analysis, and ordinary least square regression. Independent variables considered in this study include the quality of effort students made in course learning, scholarships, use of academic resources, motivation, and parents’ educational background as a socio-economic factor. The dependent variable is the overall progress students made toward desired educational outcomes. We hypothesized that engagement in course learning, scholarships, use of academic resources, motivation, and parental education have a positive relationship and predict progress toward desired educational outcomes.

The results from this study show that the ordinary least square regression model statistically predicted the progress high-achieving engineering students make towards desired educational outcomes: F (5, 50) = 2.988, p = 0.021, R = 0.50, adjusted R2 = 0.25. These results indicate a linear relationship in the sample, and the multiple regression model is a good fit for the data. The results of this study also show that course learning and motivation are significant predictors of educational progress that high-achieving engineering students make toward desired educational outcomes. The implications of the research findings and how they inform educational practices are also discussed in this paper.