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

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...

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Bibliographic Details
Published in:Sustainability 2022-02, Vol.14 (4), p.2240
Main Authors: Rosales-Torres, Conrado, Gijón-Rivera, Carlos, Garay-Rondero, Claudia Lizette, Castillo-Paz, Álvaro, Domínguez-Ramírez, Gerardo
Format: Article
Language:English
Subjects:
Access to information
Cognition & reasoning
Cognitive ability
Education
Engineering
Engineering education
Engineers
Experimentation
Higher education
Learning
Methodology
Normality
Population statistics
Problem solving
Reliability
Statistical analysis
Statistical tests
STEM education
Students
Teaching
Variance analysis
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su14042240