Design, Experimentation and Statistical Validation of a Methodology to Solve Complex Engineering Problems in Higher Education

Conrado Rosales-Torres, Carlos Gijón-Rivera, Claudia Lizette Garay-Rondero, Álvaro Castillo-Paz and Gerardo Domínguez-Ramírez
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Conrado Rosales-Torres: Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
Carlos Gijón-Rivera: Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
Claudia Lizette Garay-Rondero: Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
Álvaro Castillo-Paz: Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
Gerardo Domínguez-Ramírez: Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico

Sustainability, 2022, vol. 14, issue 4, 1-17

Abstract: This article proposes a methodology that addresses the problem that many university professors often have with their students when facing complex engineering problems, causing frustration and desertion (abandonment of the problem to be solved). Although there are antecedents of works that emphasize the relevance of the realistic context in engineering problems and the importance of being structured in solving problems, we did not find measured effectiveness from the study of a group of students. This methodology focuses on engineering problems, in such a way that the decomposition of the problems in four steps responds to the solution process of the profiles of the analyzed subjects. The process followed in the preparation, implementation, validation, and reliability of this methodology is detailed. The experiment was designed to test both the effectiveness and reliability of the methodology. Four control groups for three different courses and periods were analyzed before and after the training of the four-step methodology. The observed factor was the variable score (0–100 points). The statistical analysis comprises descriptive statistics; Normality test for each population group; Paired t -Test/Wilcoxon test, and General linear model ANOVA (2 factors). The statistical analysis and tests show how the groups involved in the experiment obtained a significant benefit when the methodology for academic performance evaluations was applied.

Keywords: complex problem solving; Education 4.0; educational innovation; STEM education; higher education; data analysis; mentoring; professional education (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:4:p:2240-:d:750654

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