A Quantitative Study of Factors Predicting High-Achieving Engineering Students’ Progress towards Desired Educational Outcomes

Ibukun Samuel Osunbunmi; Ning Fang

Download Paper | Permalink

Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Student Division (STDT) Technical Session 1: Student Success and Mentoring
Tagged Division: Student Division (STDT)
Tagged Topic: Diversity
Page Count: 11
DOI: 10.18260/1-2--42487
Permanent URL: https://peer.asee.org/42487
Download Count: 236

Paper Authors

biography

Ibukun Samuel Osunbunmi Utah State University orcid.org/0000-0002-7821-9059

visit author page

Ibukun Samuel Osunbunmi is an Assistant Research Professor, and Assessment and Instructional Specialist at Pennsylvania State University. He holds a Ph.D. degree in Engineering Education from Utah State University. Also, he has BSc and MSc degrees in mechanical engineering. His research interests include student engagement, design thinking, learning environment, evidence-based pedagogy, e-learning, broadening participation in STEM education, sustainable energy, and material characterization. This work was conducted while he was a Ph.D. student at Utah State University.

visit author page

biography

Ning Fang Utah State University

visit author page

Ning Fang is a Professor in the Department of Engineering Education at Utah State University, U.S.A. He has taught a variety of courses at both graduate and undergraduate levels, such as engineering dynamics, metal machining, and design for manufacturing

visit author page

Download Paper | Permalink

Abstract

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.

Citation
Format

Osunbunmi, I. S., & Fang, N. (2023, June), A Quantitative Study of Factors Predicting High-Achieving Engineering Students’ Progress towards Desired Educational Outcomes Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42487

TY - CPAPER
AB - 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.

AU - Ibukun Samuel Osunbunmi
AU - Ning Fang
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - A Quantitative Study of Factors Predicting High-Achieving Engineering Students’ Progress towards Desired Educational Outcomes
UR - https://peer.asee.org/42487
DO - 10.18260/1-2--42487
ER -