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.

Highlights

  • In the academic departments of science and engineering at universities, the phenomenon of students dropping out from the most demanding subjects occurs much more frequently than ever before [1]

  • Complex problem solving has topped the list in both the 2015 and 2020 World Economic Forum (WEF) report presented in its five-year report on most desired job skills, focusing on cognitive aspects [3]

  • As a general discussion about the statistical outcomes obtained, we can appreciate that in the set of observed data between groups and methodologies, the ANOVA model results strongly suggest that in the set of observed data between groups and methodologies a significant difference was observed on the average grades of students when the methodology was employed

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Summary

Introduction

In the academic departments of science and engineering at universities, the phenomenon of students dropping out from the most demanding subjects occurs much more frequently than ever before [1] In most cases this is triggered when students are asked to show their competence by solving a complex problem in a particular engineering subject [2]. In Engineering, it is common to present problems that are related to real situations for training or evaluation. Tasks such as design, optimization or application of different mechanisms (whether they are pulleys or gears in vehicles or industrial machines) are Sustainability 2022, 14, 2240.

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