How the Entry Profiles and Early Study Habits Are Related to First-Year Academic Performance in Engineering Programs

Osvaldo Aquines Gutiérrez (), Diana Margarita Hernández Taylor, Ayax Santos-Guevara, Wendy Xiomara Chavarría-Garza, Humberto Martínez-Huerta and Ross K. Galloway
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Osvaldo Aquines Gutiérrez: Department of Physics and Mathematics, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico
Diana Margarita Hernández Taylor: Department of Physics and Mathematics, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico
Ayax Santos-Guevara: Department of Physics and Mathematics, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico
Wendy Xiomara Chavarría-Garza: Department of Physics and Mathematics, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico
Humberto Martínez-Huerta: Department of Physics and Mathematics, Universidad de Monterrey, Avenida Morones Prieto 4500, San Pedro Garza García 66238, NL, Mexico
Ross K. Galloway: School of Physics & Astronomy, The University of Edinburgh, James Clerk Maxwell Building, King’s Buildings, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK

Sustainability, 2022, vol. 14, issue 22, 1-19

Abstract: This paper explores how the entry profiles of engineering students are related to their academic performance during the first year of university in a sample of 255 first-year engineering students (77 females and 178 males) at a university in Northeast Mexico. The predictors used were the high school grade point average (HSGPA), Scholastic Aptitude Test (SAT) results, the first admission test, and a Spanish adaptation of the Survey of Study Habits and Attitudes Test (SSHA) from Brown and Holtzman. The SSHA adaptation was tested for internal consistency reliabilities via Cronbach’s alpha globally (0.92) and for the following categories: delay avoidance (DA: 0.79), work methods (WM: 0.81), teacher approval (TA: 0.89), and educational acceptance (EA: 0.74). The results were compared with those of other studies to validate their consistency. To assess the different entry profiles between high- and low-achieving students, we performed a Kruskal–Wallis test and found significant differences ( p < 0.001) between both profiles for all variables. We then measured the relationships between the variables and academic success by constructing a correlation table, where HSGPA, SAT, and DA showed the highest correlations: 0.61, 0.40, and 0.36, respectively. With these outcomes, a predictive model via a logistic regression ( R 2 = 0.52 ) was built to forecast first year academic performance in the specific context.

Keywords: academic achievement; first-year university student; admission metrics; grade point average; self-efficacy; first-year university student; undergraduate students; student attitudes; correlation; predictor variables; predicting academic success; university GPA; cognitive factor; multiple regression; dropout prevention (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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