Juan Diego Delgado VargasMichael Castro Barboza202022-11-16T23:38:00Z2022-11-16T23:38:00Z2022-11-16T23:38:00Z224500247502065829192160x01010019E7D9BB16E83E4988EE8E2DEF7609C6R0lGODlhEAAQALMAAOPwwd7ttazRRd3ttLfXXtPnnPT55rzaa5LAKf///6bOOQAAAAAAAAAAAAAA
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Analíticas de aprendizaje como herramienta para la interpretación del efecto de la pandemia sobre la calificación final en la asignatura Química I de la Universidad Estatal a Distancia de Costa RicaLearning analytics as a tool for interpreting the effect of the pandemic on the final grade in Chemistry I course at the State Distance University of Costa RicaRolando Sánchez-Gutiérrez Rolando Sánchez-Gutiérrez. Químico Industrial, Máster en Gestión y Estudios Ambientales y candidato a Máster en Docencia con énfasis en Educación Superior. Investigación y publicaciones en Química, Ambiente, Gestión Ambiental, Calidad del agua, Hidrología isotópica, Legislación Ambiental y Química de Alimentos. Universidad Nacional, Costa Rica. Correo electrónico: rolando.sanchez.gutierrez@una.ac.cr https://orcid.org/0000-0002-5050-021X. HYPERLINK "mailto:rolando.sanchez.gutierrez@una.ac.cr" rolando.sanchez.gutierrez@una.ac.crUniversidad Nacional, Costa Rica Wendy Villalobos-González Wendy Villalobos-González. Química Industrial, Máster en Gestión y Estudios Ambientales. Investigación y publicaciones en Química, Ambiente, Gestión Ambiental, Calidad del agua, Calidad del aire, Riesgos Químicos y Ambientales en la Industria Gráfica, Legislación Ambiental, Educación en Química y Enseñanza de la Química. Universidad Estatal a Distancia, Costa Rica. Correo electrónico: wvillalobosg@uned.ac.cr https://orcid.org/0000-0002-3660-0169. HYPERLINK "mailto:wvillalobosg@uned.ac.cr" wvillalobosg@uned.ac.crUniversidad Estatal a Distancia, Costa RicaDOI: http://dx.doi.org/10.22458/caes.v13iEspecial.4489Volumen 13, Número Especial30 de noviembre de 2022pp. 127 – 148Recibido: 26 de febrero de 2022Aprobado: 18 de marzo de 2022Resumen Debido a la Covid-19, las instituciones de educación deben migrar sus prácticas tradicionales a la modalidad virtual. Esto representa retos importantes en la planificación, mediación y evaluación de las asignaturas. Las analíticas de aprendizaje son un conjunto de herramientas innovadoras en el área pedagógica, en la que el uso de datos puede contribuir al mejoramiento de las asignaturas y políticas educativas. En este trabajo se pretende realizar una reflexión descriptiva de las analíticas de aprendizaje valorando el efecto de la pandemia en el aprovechamiento de la asignatura. Para esto, se realiza una investigación de tipo cuantitativo mediante el estudio de las calificaciones en la asignatura de Química I durante el periodo 2018-2021 en la UNED, Costa Rica. El aprovechamiento general de la asignatura experimenta un aumento en la calificación promedio durante el periodo 2020-2021; además, existen diferencias significativas entre los rendimientos de acuerdo con las regiones demográficas del país. Sin embargo, es necesario realizar esfuerzos que permitan medir la calidad del aprendizaje logrado en la asignatura y el grado de satisfacción de los estudiantes con lo alcanzado en la misma. Palabras claves Analíticas de aprendizaje, Pandemia, Química, Calificación. Abstract Due to Covid-19, educational institutions had to change traditional educational practices to virtual modality, representing significant challenges on the planning, mediating, and evaluating processes. Learning analytics are a set of innovative tools in the pedagogical area, in which the use of data can contribute to the improvement of courses and educational policies. This work aims to carry out a descriptive reflection of the learning analytics evaluating the effect of the pandemic on the overall performance of the subject. For this purpose, a quantitative investigation was carried out by studying the grades in the subject of Chemistry I during the 2018-2021 period at the UNED, Costa Rica. The overall performance and approval of the subject experienced an increase in the average grade during the period 2020-2021. In addition, some significant differences were found between the mean grades according to the demographic regions of the country. However, it is necessary to make efforts in order to measure the quality of the learning achieved in the subject and the degree of satisfaction of the students according to the learning objectives that have been achieved in the course. Keyword Learning analytics, Pandemic, Chemistry, GradesÁrea temática Hacia una cultura de gestión de la calidad académica. Subcontenido (s) Indicadores de gestión de la calidad académica. Introducción La enseñanza remota de emergencia (ERE) es un cambio temporal en la entrega de la docencia a un modo alternativo en circunstancias de crisis (Hodges et al., 2020). La ERE generada por el virus SARS-CoV-2 ha exigido a las instituciones dedicadas a la educación, migrar las prácticas educativas tradicionales a la modalidad virtual, mediada a través del uso de las tecnologías de la información y la comunicación (TIC) y las tecnologías del aprendizaje y del conocimiento (TAC). Los entornos de aprendizaje son un recurso que forman parte de estas tecnologías, los cuales producen grandes cantidades de datos (Cerro et al., 2020).En la última década, el uso de datos generados por las personas usuarias de diversos sistemas de información ha tenido un auge importante. El análisis gráfico y numérico de datos posibilita la obtención rápida de la información que ayudan de forma conjunta en la toma de decisiones (Kitchin, 2014). En el área educativa, el análisis de datos ha sido utilizado con dos finalidades (una descriptiva y otra predictiva), y con el propósito de revelar y contextualizar la información, comprender y optimizar el aprendizaje y predecir datos y patrones futuros de los procesos de aprendizaje (Cerro et al., 2020; Ferguson et al., 2016). Las analíticas de aprendizaje (LA, por sus siglas en inglés) son un conjunto de herramientas innovadoras en el área pedagógica, en las que la invención y el uso de datos pueden contribuir a resolver problemas prácticos. Se considera que estas toman importancia en el año 2011, durante la primera Conferencia Internacional sobre Analíticas de Aprendizaje y Conocimiento (Ferguson et al., 2016). Dentro de los beneficios de las LA se pueden citar: la implementación de sistemas de gestión del aprendizaje, la toma de decisiones impulsadas por el análisis de los datos, la identificación de patrones de comportamiento en los estudiantes, el mejoramiento de los procesos de aprendizaje, la identificación de alumnos que necesitan apoyo, predecir el comportamiento futuro de los alumnos y el diseño y planificación de intervenciones educativas (Avella et al., 2016; Ferguson et al., 2016; Scheffel et al., 2019).Según Cerro et al. (2020) existen desafíos de origen técnico, pedagógico y político/económico durante la implementación de LA. El primero está relacionado con trabajar grandes volúmenes de datos, mismo que puede ser resuelto utilizando herramientas informáticas (Ferguson et al., 2016). El segundo es el desafío pedagógico para la búsqueda de soluciones que permitan optimizar el aprendizaje de las personas estudiantes. Por último, la parte política y económica es importante para la puesta en marcha de las innovaciones educativas (Cerro et al., 2020); no obstante, existe una escasez de políticas que guíen la forma segura de implementar LA en cuestiones legales, éticas y de privacidad de la información (Scheffel et al., 2019).En América Latina, los avances para la adopción de LA han representado una oportunidad de mejora en los contextos de planificación durante los últimos años, debido al fuerte enfoque hacia el aseguramiento de la calidad en los procesos de aprendizajes en instituciones de educación superior de la región (Hilliger et al., 2020). No obstante, y hasta el momento, varios de los estudios publicados al respecto centran la atención en los aspectos de identificación y aplicación de metodologías mixtas para la adopción de estas herramientas en un contexto universitario general (Ortiz-Rojas et al., 2019), mientras que estudios sobre la adopción de estas estrategias en una menor escala (específicamente en asignaturas particulares), no son comunes en nuestra región. Sin embargo, varios estudios desarrollados en otras regiones han mostrado los avances de LA mediante distintos enfoques en asignaturas de particular interés, como lo son las asignaturas introductorias de Química (Chen y Li, 2021; Deng et al., 2019; Reyes et al., 2020; Van Horne et al., 2018).En este trabajo se pretende realizar una reflexión descriptiva de las LA obtenidas con los registros de calificaciones en la asignatura de Química I durante el periodo 2018-2021. La finalidad es valorar el efecto de la pandemia en la calificación final de la asignatura, incorporando el factor de las regiones del país donde se encuentra el centro universitario como una variable de estudio debido a las diferencias sociales, económicas, culturales y educativas inherentes de cada región. Lo anterior como un insumo para la toma de decisiones futuras en la planificación, mediación y evaluación de los aprendizajes en esta asignatura.Método Se realiza una investigación de tipo cuantitativo y de enfoque secuencial (Hernández-Sampieri y Mendoza-Torres, 2018) mediante el estudio de los datos de récord académico para la asignatura de Química I impartida en la Universidad Estatal a Distancia de Costa Rica (UNED). Este estudio se desarrolla como parte de las iniciativas de la Cátedra de Ciencias Química orientadas en la gestión de la calidad académica de las asignaturas ofertadas.Población de estudio El alcance de este estudio se centra en las notas del récord académico proveniente del sistema de notas parciales de la UNED durante el periodo 2018-2021. Se utiliza una muestra comprendida por 5 cuatrimestres en periodo prepandemia (IIC-2018 al IIIC-2019; 1266 participaciones) y 5 cuatrimestres en periodo pandemia (IC-2020 al IIIC-2021; 1569 participaciones). Además, se utiliza como variable categórica la región del país donde se encuentra el centro universitario (CEU) de donde proviene la participación, esto con la finalidad de analizar un posible impacto diferenciado por regiones. Para cumplir con el manejo ético de los datos, las personas autoras únicamente acceden a los datos relativos a calificaciones finales de la asignatura de Química I comprendidos entre el periodo consignado anteriormente.Procedimiento de análisis de datosSe realiza un análisis estadístico descriptivo sobre las calificaciones y el estado de aprobación de la asignatura analizando estas variables de acuerdo con la región geográfica en el país del CEU y periodo de estudio (prepandemia o pandemia). Posteriormente, se realiza el análisis de inferencia estadística utilizando modelos de regresión mediante las medias marginales estimadas de los modelos con el objetivo de identificar diferencias en las notas de la asignatura de acuerdo con el periodo de estudio, región geográfica o la interacción de ambos factores a través del contraste de medias (Games et al., 1981; Searle et al. 1980). Para el procesamiento, análisis de la información y producción gráfica de resultados se utiliza el lenguaje de programación R 3.6.5 (R Core Team, 2020), utilizando paquetes como ggplot2 (Wickham, 2016), emmeans (Length et al., 2019) y vtree (Barrowman et al., 2021). Resultados En la Figura 1, se muestra la distribución por regiones de los 2835 estudiantes matriculados en la asignatura Química I durante el periodo 2018-2021; así como datos estadísticos descriptivos de récord académico en la asignatura Química I. Figura 1. Distribución espacial de la población de estudio y comportamiento del récord académico por regióniVBORw0KGgoAAAANSUhEUgAAA1sAAAODCAYAAABKURtHAAAAAXNSR0IArs4c6QAAAARnQU1BAACx
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Fuente: elaboración propia.En la Figura 2, se observa una descripción general de la población en estudio según el estado de la asignatura y los indicadores estadísticos como media, mediana, rango y las desviaciones estándar. El porcentaje de aprobación, reprobación y retiro para el periodo estudiado de pre-pandemia fue de 30%, 66% y 4%, respectivamente. En contraste, el porcentaje de aprobación, reprobación y retiro para el periodo analizado de pandemia fue de 44%, 50% y 5%, respectivamente. Asimismo, se puede advertir un aumento en la nota promedio de la asignatura para el periodo pandemia, tanto a nivel general como por cada estado de la asignatura.Figura 2. iVBORw0KGgoAAAANSUhEUgAABVYAAAMACAIAAABAXKuVAAAAAXNSR0IArs4c6QAA/8pJREFUeF7s
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Descripción general de la población en estudio según el estado de la asignatura y periodo de estudioFuente: elaboración propia.El análisis inferencial para determinar si existe una diferencia significativa en el periodo de estudio (pandemia y pre-pandemia) se muestra en la Figura 3. La distribución de datos por periodo presenta un comportamiento similar: el valor medio para la nota en el periodo pandemia es mayor. Lo anterior sugiere la existencia de una diferencia entre estos lapsos, lo cual se confirma con el análisis de contraste a través de las medias marginales estimadas con el modelado (p<0.05) que se muestran en la Tabla 1. Figura 3. Distribución de notas por periodo de estudioiVBORw0KGgoAAAANSUhEUgAAAqcAAAHeCAIAAAFeRQVRAAAAAXNSR0IArs4c6QAAAARnQU1BAACx
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Fuente: elaboración propia.Tabla 1. Medias marginales estimadas para las notas de la asignatura por periodo de estudioPeriodoMediaError estándarIntervalo de confianzaPandemia(n=1569)5.160.075.025.29Prepandemia(n=1266)4.330.084.184.48En la Figura 4, se presenta la distribución de notas por región demográfica. Estas muestran un comportamiento muy similar entre sí. Algunas regiones específicas evidencian una congruencia mayor, por ejemplo, la Central y la Brunca, así como la Huetar y la Pacífico. No obstante, en relación con la media marginal estimada por el modelo generado (p<0.05) y el error estándar asociado (Tabla 2), se observa que estos últimos son más amplios que el caso del estudio de los periodos (Tabla 1). Figura 4. Distribución de notas de acuerdo con la región geográfica del paísiVBORw0KGgoAAAANSUhEUgAABVYAAAMACAIAAABAXKuVAAAAAXNSR0IArs4c6QAA/8pJREFUeF7s
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Fuente: elaboración propia.Tabla 2. Medias marginales estimadas para las notas de la asignatura por regiónRegiónMediaError estándarIntervalo de confianzaBrunca(n=550)5.100.124.875.33Central(n=1449)4.750.074.614.89Chorotega(n=103)4.180.273.654.72Huetar Atlántica(n=272)4.720.174.395.05Huetar Norte(n=336)4.600.154.304.89Pacífico Central(n=125)5.070.254.595.55Asimismo, en la Figura 5, se observa que no existe una diferencia estadísticamente significativa entre las categorías del factor en estudio. Únicamente la Región Chorotega y la Brunca presentan una diferencia estimada distinta de cero.Figura 5. Contrastes para las medias marginales entre regionesiVBORw0KGgoAAAANSUhEUgAABVYAAAMACAIAAABAXKuVAAAAAXNSR0IArs4c6QAA/8pJREFUeF7s
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Fuente: elaboración propia. Por último, en la Figura 6, se advierte la interacción de los factores periodo de estudio y región. Se puede inferir que, en todas las regiones del país, existe un cambio en la distribución de las notas de acuerdo con el periodo, en el que existe una tendencia a una mayor frecuencia hacia valores más altos. Únicamente para la Región Central este cambio no es tan marcado en comparación con las demás regiones. Por su parte, en la Tabla 3, figuran las medias marginales estimadas con el modelado (p<0.05), para el estudio de la interacción de los factores periodo de estudio y región. Figura 6. Interacción entre los factores periodo de estudio y región/9j/4AAQSkZJRgABAQEA3ADcAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYF
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2R==
Fuente: elaboración propia Tabla 3. Medias marginales estimadas para las notas de la asignatura por región y periodo de estudioRegiónPeriodonMediaError estándarIntervalo de confianzaBruncaPrepandemia2594.720.174.395.05Central6834.430.104.224.63Chorotega473.780.403.004.55Huetar Atlántica1024.040.273.524.57Huetar Norte1363.620.233.164.07Pacífico Central394.090.443.244.94BruncaPandemia2915.430.165.125.74Central7665.040.104.855.23Chorotega564.530.363.825.24Huetar Atlántica1705.130.214.725.53Huetar Norte2005.260.194.895.64Pacífico Central865.520.294.946.09 En la Figura 7, se ofrecen los contrastes para la interrelación de las tres variables. Se evidencia que, de los sesenta y seis contrastes calculados, cerca de un 26 % (17 sobre 66) presenta una diferencia significativa para la diferencia estimada en sus medias marginales. De este modo, se advierte un efecto relativamente importante del factor periodo sobre las categorías del factor región. Figura 7. Contrastes para las medias marginales entre regiones y periodo de estudio (P = pandemia, PP= prepandemia)iVBORw0KGgoAAAANSUhEUgAAApEAAAIZCAMAAADJFpRMAAAA81BMVEUAAAAAADoAAGYAOjoAOmYA
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Fuente: elaboración propia. Además, se puede observar que el factor periodo tiene efecto dentro del factor región solamente para dos de los casos. Así pues, es posible afirmar que las otras cuatro regiones del país no presentan una diferencia estadísticamente significativa en la nota media de la asignatura a causa de la pandemia. Sin embargo, en relación con las regiones en las que se registra un efecto, es importe señalar que ambas aportan un 63% de la población en estudio.Discusión El aprovechamiento general de la asignatura de Química I en la Universidad Estatal a Distancia de Costa Rica ha experimentado un aumento en la calificación promedio durante el periodo 2020-2021. Esto último influenciado por los cambios implementados en la evaluación de la asignatura, ya que un componente importante fue aplicado de manera virtual a raíz de la emergencia sanitaria nacional debido a la Covid-19, lo cual tuvo un efecto positivo sobre los datos de aprobación en contraste con datos de aprovechamiento para el período 2018-2019 analizado en este estudio y los datos del periodo 2010-2017, que mostraban un promedio de aprobación en la asignatura de un 20.2 %, un 62.2 % de reprobación y 17.6 % de retiro (Villalobos y Villalobos, 2018).Un enfoque alternativo para entender el efecto de la pandemia en la asignatura de Química es el presentado por Van Heuvelen, Daub y Ryswik (2020). Estos investigadores centran la atención sobre el porcentaje de evaluaciones sometidas a calificación y no el desempeño del estudiante, lo anterior justificado en las particularidades emocionales que pueden experimentar las personas estudiantes debido a las medidas de confinamiento y distanciamiento social. Además, a través de este enfoque, también puede ser medido el porcentaje de deserción estudiantil y, en cierta medida, relacionarlo con las facilidades que el estudiantado posee para poder continuar con sus estudios en tiempos de pandemia, lo cual se ha convertido en un especial desafío para asignaturas relacionadas con el área de Química (Burnett et al., 2020; Sunasee, 2020; Tigaa y Sonawane, 2020).En relación con el factor de la zona demográfica del país, los resultados evidencian distribuciones muy similares para las seis regiones, donde las diferencias en la media de calificación oscilan desde 0.03 hasta 0.92. Estos datos son útiles para identificar los CEU con población estudiantil más vulnerable y con mayores necesidades en la asignatura de Química. Datos del Octavo Informe sobre el Estado de la Educación en Costa Rica (Programa Estado de la Nación, 2021) revelan no solo un rezago en la educación primaria y secundaria de nuestro país, sino que, también, señalan la existencia de brechas importantes para centros de estudios urbanos y rurales. Ciertamente, esto determina los rendimientos académicos de estudiantes que son atendidos en las diversas sedes de la UNED. Como posibles causas podemos citar las limitaciones de acceso a 1) educación secundaria de calidad que influye en la adquisición de conocimientos básicos de ciencias y 2) recursos tecnológicos lo que limita el acceso a recursos digitales y plataformas de aprendizaje; a partir de este análisis es posible plantear la necesidad de reforzar el área básica de conocimiento de la Química en las regiones; así como de investigar con mayor profundidad las causas de esta diferencia encontrada. Por otra parte, el análisis estadístico para la interacción de las variables categóricas en estudio muestra distribuciones con valores más altos en la calificación promedio de la asignatura en el periodo de pandemia para las seis regiones demográficas del país. En los contrastes de las medias marginales estimadas, se evidencia que únicamente se encuentran diferencias significativas entre periodos para una misma región en los casos de la Región Central y la Huetar Norte. Sin embargo, en estas dos, se encuentra el 63 % de la población total en estudio, lo que implica un efecto positivo e importante que ha sido observado en otros casos de estudio (Burnett et al., 2020); no obstante, también plantea un reto en el cual se pueda identificar a través de herramientas de LA, si el impacto es realmente sobre los objetivos de aprendizaje, habilidades y destrezas que se pretenden formar en la asignatura (AlMahdawi, Senghore, Ambrin y Belbase, 2021).Finalmente, es importante señalar que las herramientas de LA pueden ser utilizadas, no solo como un medio que coadyuve para la toma de decisiones, antes o después de la entrega de una asignatura, sino como un medio para realizar un análisis de datos. Estudios demuestran la aplicación de estas herramientas durante la asignatura como medio de realimentación al estudiante y de monitoreo en tiempo real del progreso y desempeño en la asignatura. De tal manera, se tiende a motivar a los estudiantes para promover procesos de aprendizaje con mayor autorregulación por parte del estudiante que, a su vez, permiten identificar las áreas en las que las actividades de aprendizaje están siendo más eficaces (Deng et al., 2019; Van Horne et al., 2018).Conclusiones y recomendaciones De manera general, es posible indicar que los cambios generados para la mediación y evaluación de la asignatura de Química I impactan de manera positiva sobre la calificación y el nivel de aprobación de los estudiantes; sin embargo, es necesario realizar esfuerzos que permitan medir la calidad del aprendizaje logrado en la asignatura y el grado de satisfacción de las personas estudiantes con lo alcanzado en la asignatura, ya que es sesgado indicar que esto se alcanza únicamente debido al resultado final de la asignatura, el cual puede estar en mayor medida influenciado por la aplicación de un componente importante de la evaluación de forma virtual. Es importante rescatar que la aplicación de herramientas de LA es imperativa para evidenciar esta información que permite plantear y priorizar las acciones necesarias para mantener en constante mejora la planificación, mediación y evaluación de las asignaturas. Aunado a lo anterior, se recomienda medir de manera cualitativa y cuantitativa el nivel de satisfacción de los estudiantes en relación con las acciones tomadas en la asignatura y con el nivel de aprendizaje logrado. Además, paralelamente, es importante monitorear otras variables de la asignatura que puedan ser fácilmente interpretadas con el uso de las LA, tales como participación en los ambientes virtuales, tiempos dedicados a los recursos suministrados, tiempo requerido para evaluaciones y su relación con las calificaciones, entre otros.Referencias bibliográficas Almahdawi, M., Senghore, S., Ambrin, H y Belbase, S. (2021). 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________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Analíticas de aprendizaje como herramienta para la interpretación del efecto de la pandemia sobre la calificación final en la asignatura Química I de la Universidad Estatal a Distancia de Costa RicaRolando Sánchez-Gutiérrez / Wendy Villalobos-GonzálezDOI: http://dx.doi.org/10.22458/caes.v13iEspecial.4489 /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAIBAQIBAQICAgICAgICAwUDAwMDAwYEBAMFBwYHBwcG
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Artículo protegido por licencia Creative CommonsPAGE \* MERGEFORMAT4REVISTA ELECTRÓNICA CALIDAD EN LA EDUCACIÓN SUPERIOR/ISSN:1659-4703, VOL.13 (Especial) JUNIO-DICIEMBRE, 2022: 127-148http://revistas.uned.ac.cr./index.php/revistacalidadCorreo electrónico: revistacalidad@uned.ac.cr__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________Analíticas de aprendizaje como herramienta para la interpretación del efecto de la pandemia sobre la calificación final en la asignatura Química I de la Universidad Estatal a Distancia de Costa RicaRolando Sánchez-Gutiérrez / Wendy Villalobos-GonzálezDOI: http://dx.doi.org/10.22458/caes.v13iEspecial.4489 Artículo protegido por licencia Creative CommonsPAGE \* MERGEFORMAT1