Abstract
Contribution: A methodology based on the learning by teaching approach facilitated by a mobile learning tool for creating tutorials. A study of the impact this methodology has had on the emotional well-being of students and its correlation with academic performance has also been carried out. Background: Insufficient resources and the absence of targeted teaching methods for technical content in vocational training may impact academic outcomes and lead to student demotivation. Previous studies indicate that employing active group methodologies contributes to the improvement of educational quality and positively influences the emotional well-being of students. Intended Outcomes: Improved academic performance, motivation levels, and collaborative work among peers, particularly within vocational training cycles. Application Design: A quasi-experimental design with a pre–post assessment of knowledge and emotional states. The study was carried out with 131 vocational training students, with one group following a conventional master class methodology that used practical activities, and another group following the learning by teaching methodology, where students created explanatory tutorials for their peers using a mobile application. Findings: Students who followed the proposed methodology significantly improved learning results compared to those who followed the traditional methodology. This improvement was evident both at the end of the experience and in the final evaluation of the course. Moreover, results revealed that the emotions of enjoyment and pride at the end of the learning process positively correlated with the acquisition of knowledge and that this correlation was more pronounced within the cohort that followed the learning by teaching methodology.
Isaac Lozano-Osorio
Phd in Artificial Intelligence
Isaac Lozano graduated with a double degree in Computer Engineering and Computer Engineering from the Universidad Rey Juan Carlos, where he was awarded the prize for the Best Final Project. Subsequently, he completed a Master in Artificial Intelligence Research (UIMP). His main research interests are focused on the interface between Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristic procedures for graph modeled optimization problems.