In sustainable education, it is important to analyze student diversity in order to create strategies that allow for the implementation of inclusive education based on the differences observed among students. To achieve this, a sample of 321 first-year engineering students (107 females and 214 males) at a private university in northeast Mexico was analyzed during the 2020 academic year. Students were classified according to their gender, engineering program, and the development of their multiple intelligences according to Howard Gardner theory of multiple intelligences. To verify the effect of gender and program factors on the development of multiple intelligences, Kruskal–Wallis tests were performed with α = 0.05. The analysis of the effects of gender identified significant differences in four intelligences: linguistic and interpersonal (for which the female students obtained higher mean scores) and mathematical and visual (for which the male students obtained higher mean scores). The analysis of the effects of the engineering program identified significant differences in five intelligences: mathematical, visual, and musical (for which civil engineering students obtained a higher mean score than the students in the other programs); kinesthetic (for which computer science students obtained a lower mean score than students in the other programs); and naturalistic (for which sustainability engineering students obtained a higher mean score than students in the other programs). These differences allowed us to observe the characteristics of the students and to develop more inclusive courses in order to make the teaching and learning process more optimal and sustainable.

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 (R2=0.52) was built to forecast first year academic performance in the specific context.