Learning in Project-based Engineering Education: A bibliometric Analysis

Abstract

The events caused by the COVID-19 pandemic forced educators to transition to a fully online education system. Therefore, the implementation of project-based learning (PBL) in education is not an easy task. However, since PBL offers students many learning benefits, the aim of this paper is to conduct a bibliometric analysis to measure the performance of using PBL in engineering courses. Two computer tools have been used to carry out the analysis: a) Scopus as a performance analysis tool and b) SciMAT to carry out the bibliometric analysis of content from scientific maps. The results show that the topic with the highest performance for the period 2000-2015 corresponds to the topic of engineering educators, with a h-index of 19, 157 linked articles and, 4815 citations. However, the transition to digital teaching is shown in the period 2016-2021, where e-learning systems represent a cross-cutting theme in the field of research. Therefore, the close relationship of the PBL methodology with engineering educators and the transition to virtual education is shown. However, it requires the integration of e-learning platforms. This is closely related to the driving topics shown in the strategic map for the 2016-2021 period, such as: ensuring the quality of teamwork; properly monitor learning experiences; enhance the focus on engineering design; and work on planning the design of the curriculum, to involve all the activities and evaluations of the educational goals.

Performance of Image Recognition with Machine Vision: A Systematic Review

“…El método híbrido se trata de la implantación de dos técnicas diferentes con la finalidad de generar mejores resultados [18]. Por otro lado, Background Sustractor es una técnica muy utilizada en aplicaciones de detección de personas, donde se analiza cada uno de los objetos de la imagen y determina si corresponden al fondo o al cuadro de la imagen [19].…”

“…Este trabajo de revisión sistemática se realizó siguiendo la guía práctica del flujograma de la Declaración PRISMA, siguiendo la metodología propuesta en [13]- [18]; esto con el objetivo de determinar los diferentes algoritmos que existen para el reconocimiento de imágenes aplicando técnicas de visión artificial, mediante la inclusión de artículos científicos originales realizados entre los años 2020 al 2022.…”