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https://doi.org/10.37815/rte.v34n3.957

Artículos originales

 

 

Desarrollo de un XBlock en Open edX para a= poyar el monitoreo y seguimiento en un SPOC

Developme= nt of a XBlock in Open edX to support monitoring and follow-u= p in a SPOC

 

Jonnathan Campoberde= 1 <= /span>https://orcid.org/0000-0= 002-4998-3875,

Miguel Á. Macía= s1  = https://orcid.org/0000-0= 002-8007-7301, Jorge Maldonado-Mahauad1 https://orcid.org/0000-0= 003-1953-390X

 =

1Universidad de Cuenca, Cuenca, Ecuador

jonnathan.campoberde@gmail.com, mangel.maciasn@gmail.com, jorge.maldonado@ucuenca.edu.ec

 

Enviado:         2022/07/03

Aceptado:       2022/09/19

Publicado:      2022/11/30                         

Resumen

Sumario: Introducción, Metodología, Discusión de los Resultados y Conclusiones.

 <= /o:p>

Como citar:<= /span> Campoberde, J., Macías, M. & M= aldonado-Mahauad, J. (2022).

Desarrollo d= e un XBlock en Open edX para apoyar el monitoreo y seguimiento en un SPOC. Revista Tecnológ= ica - Espol, 34(3), 139-156. http://www.rte.espol.e= du.ec/index.php/tecnologica/article/view/957


Los Cursos Masivos Abiertos y en Lí= nea (MOOC) se han convertido en una tecnología disruptiva que ha buscado democratizar el acceso a la educación. Estos son cursos que se ofertan de f= orma abierta generalmente en alguna plataforma para MOOC como es Open edX.  Estos c= ursos son tomados por cientos de miles de estudiantes, quienes lo siguen de forma autónoma, esto es, sin la presencia o guía de un docente. Cuando un MOOC se cierra para un número menor de estudiantes y se utiliza de forma privada integrándolo al currículo académico, se los conoce como Cursos Pequeños Privados en Línea (SPOC). Los SPOC, a diferencia de los MOOC, requieren la presencia y guía de un docente mientras los estudiantes toman el curso. Sin embargo, la plataforma Open edX carece de visualizaciones que asistan a estudiantes y docentes en la toma de decision= es durante el transcurso del curso. Es decir que el seguimiento y el monitoreo= es escaso y limitado. Por este motivo, el presente trabajo propone desarrollar un componente denominado XBlock que implemente, por un lado, visualizac= iones para estudiantes a fin de dar cuenta de su proceso de aprendizaje; y, por o= tro lado, visualizaciones para docentes a fin de que puedan monitorear, hacer seguimiento y retroalimentar a los estudiantes en un SPOC. Para lograrlo se empleó la metodología adaptada de LATUX para la planificación, diseño e implementación de un XBlock y evaluación= de las visualizaciones del dashboard. Como resulta= do de la evaluación, se evidenció que cerca del 80% de los estudiantes perciben a= l XBlock desarrollado como un estímulo positivo = para redirigir el comportamiento de los estudiantes. Además, contribuye como un apoyo para los docentes al momento de diseñar estrategias de enseñanza que permitan hacer el monitoreo y retroalimentar de mejor manera a los estudian= tes para que puedan terminar con éxito el curso.

 

= Pa= labras clave: = MOOC, indicadores, dashboard, SPOC, patrones de apren= dizaje.

 

Abstract

Massive Open Online Courses (MOOCs) have become a disruptive technology that has aimed to democratize access to education. Th= ese are courses that are offered openly, generally on a MOOC platform such as O= pen edX. These are courses that are offered openly, generally on a MOOC platform such as Open edX, and are taken by hundreds of thousands of students autonomously (without the presence or guidance of a teacher). When a MOOC is closed to a smaller number of students and is used privately and integrated into the academic curriculum, it is known as a Small Private Online Course (SPOC). SPOCs, unlike MOOCs, require the presence and guidance of a teacher= while students take the course. However, the Open edX platform lacks visualizatio= ns to assist students and teachers in making decisions during the course. In o= ther words, follow-up and monitoring is scarce and limited. For this reason, the present work proposes to develop a component called XBlock that implements,= on the one hand, visualizations for students in order to account for their learning process; and, on the other hand, visualizations for teachers so th= at they can monitor, follow up and give feedback to students in a SPOC. To ach= ieve this, the methodology adapted from LATUX was used for the planning, design = and implementation of an XBlock and also for the evaluation of the visualizatio= n of the dashboard. As a result of the evaluation, it was found that about 80% of the students perceive the developed XBlock as a positive stimulus to redire= ct student behavior. In addition, it contributes as a support for teachers when designing teaching strategies that allow them to monitor and provide better feedback to students so that they can successfully complete the course.

 

Keywords: MOOC, indicat= ors, dashboard, SPOC, learning patterns.

 

Introducción

En la actualid= ad los usuarios al interactuar con los sistemas de información generan una gran cantidad de datos, dejando como resultado una huella digital (Ben Kei, 2016). En el sector educativo, en los últimos = años, los Sistemas de Gestión de Aprendizaje (SGA) se han convertido en una fuente rica de datos, producto de las interacciones de los estudiantes con los recursos de un curso. Un ejemplo de esto, son los Cursos Masivos Abiertos y= en Línea, conocidos en inglés como MOOC. Estos cursos atraen a una gran cantid= ad de estudiantes de todo el mundo (Shah, 2020)(Dhawal Shah, 2020)(Torre et al., 2020)(Torre et al., 2020)(Antonaci et = al., 2018). Este proyecto, incorpora visualizacione= s con el objetivo de conocer las interacciones de cada estudiante dentro de la plataforma. Otro ejemplo es OXALIC, este proyecto provee de visualizaciones= que se presentan solo a los docentes, pero únicamente sobre información general= de los estudiantes y su progreso en el curso (Khalil & Belokrys, = 2020)(Antonaci et = al., 2018), limitando las posibilidades de los SPOC a un e-learning tradicional.=

 

Por lo anterio= r, en este trabajo se propone desarrollar un componente denominado XBlock que implemente, por un lado, visualizac= iones para estudiantes a fin de dar cuenta de su proceso de aprendizaje; y, por o= tro lado, visualizaciones para docentes a fin de que puedan monitorear, hacer seguimiento y retroalimentar a los estudiantes en un SPOC. Para esto: (1) se parte del desarrollo de un análisis exploratorio sobre el comportamiento de los estudiantes en un SPOC para (2) determinar variables y secuencias de aprendizaje comunes; (3) se propone un diseño de un da= shboard de visualizaciones para profesores y estudiantes a partir de las variables y secuencias detectadas; (4) se implementa el dashboard<= /span> bajo la forma de un XBlock para la plata= forma abierta Open edX que permita visualizar el comportamiento de los estudiantes; y (5) finalmente, se hace una evaluación local para analizar la usabilidad de las visualizaciones desarrolladas en el = XBlock. Como resultado se evidenció un alto grado de aceptación y conformidad en el= uso de las visualizaciones para la toma de decisiones por parte de estudiantes y docentes al utilizar el XBlock, así como también el interés por hacer seguimiento de su comportamiento a lo largo de= las semanas mediante las visualizaciones del dashboard.

 

El artículo se encuentra estructurado de la siguiente manera: en la sección 2 se describe = la metodología utilizada para el diseño, desarrollo y evaluación del dashboard desarrollado en el = XBlock. La sección 3 presenta la discusión de los resultados de evaluación. Finalme= nte, la sección 4 presenta las principales conclusiones.

 

Metodología

Para el desarr= ollo del XBlock para la plataforma Open edX se ha empleado la metodología adaptada de LATU= X (Learning Awareness Tool – User eXperience) (Martine= z-Maldonado et al., 2016). Esta metodología ha sido seleccionada d= ebido a que es útil en los contextos donde se diseñe e implemente herramientas que mejoren la entrega de información en entornos de aprendizaje virtual. Las c= inco etapas con las que cuenta la metodología se agrupan bajo 2 enfoques: el pri= mer enfoque se centra en la identificación del problema y sus indicadores; y el segundo enfoque aborda las etapas de planificación, diseño, implementación y evaluación. La Figura 1 presenta los pasos de la metodología ada= ptada de LATUX.

 

Figura = 1=

Pasos de la Metodología Ad= aptada LATUX Empleada en el Desarrollo del XBlock          ¿Qui= énes son los interesados?

·         ¿Cuá= les son las necesidades de los interesados?

·         ¿Qué fuentes de datos se dispone?

·         ¿Cuá= l es el contexto de aprendizaje?

·         ¿Qué herramientas de Analítica de Aprendizaje se dispone?

·         ¿Cómo están los datos siendo obtenidos, usados, compartidos y almacenados?

 

Como resultado= de este análisis, se identificaron características a ser implementadas a través de visualizaciones que apoyen a los estudiantes y a los docentes en un SPOC, y= que están relacionadas con (i) conocer cuál es su situación actual dentro del curso, ya que esto le permite poder tomar decisiones para corregir o reforz= ar su proceso de aprendizaje; (ii) la necesidad de contrastar su rendimiento con otros compañeros dentro del mismo curso, esto como una motivación a mejorar o mantenerse dentro de aquel porcentaje de estudiantes que están teniendo éxito; (iii) el = nivel de compromiso que pueden adquirir los estudiantes al interactuar con los recursos dentro de un curso.

 

Este nivel de compromiso ha sido estudiado y organizado en tres dimensiones, tales como, medidas de comportamiento, medidas cognitiva y medidas emocionales <= !--[if supportFields]>ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemDa= ta":{"DOI":"10.1109/TLT.2020.3003220","ISSN&q= uot;:"19391382","abstract":"Recent research shows that learners who are able to self-regulate their learning s= how greater levels of engagement with massive open online course (MOOC) content= . To improve support for learners in their self-regulatory processes, researchers have proposed technological solutions to transform recorded MOOC data into actionable knowledge. However, studies providing empirical evidence on how these solutions impact learners' engagement with the course and their self-regulatory behavior remain scarce. In this article, we present the res= ults of an observational case study in which NoteMyProgress (NMP), a web-based t= ool designed to support learners' self-regulation in MOOCs, is applied as an intervention in two MOOCs. The main aim of this article is to provide insig= hts into how the support of learners' self-regulated learning (SRL) strategies correlates with course engagement. We performed the evaluation using a samp= le of 263 learners and utilized distinct data sources in order to propose indicators for learners' engagement with the course and NMP. Results show a positive correlation between learners' final grades with NMP functionalities that support goal setting, organization (note taking), and self-reflection (social comparison) SRL strategies. Furthermore, we found no significant behavioral differences in how learners with low SRL and high SRL profiles engage with the course or NMP. Finally, we discuss how these results relate= to prior work and the implications for future technological solutions that see= k to promote engagement in MOOCs.","author":[{"dropping-particle":""= ;,"family":"Perez-Alvarez","given":"Rona= ld Antonio","non-dropping-particle":"","parse-na= mes":false,"suffix":""},{"dropping-particle&q= uot;:"","family":"Maldonado-Mahauad","gi= ven":"Jorge","non-dropping-particle":"",= "parse-names":false,"suffix":""},{"dropp= ing-particle":"","family":"Sharma","= ;given":"Kshitij","non-dropping-particle":"&q= uot;,"parse-names":false,"suffix":""},{"= dropping-particle":"","family":"Sapunar-Opazo= ","given":"Diego","non-dropping-particle"= ;:"","parse-names":false,"suffix":""= ;},{"dropping-particle":"","family":"Per= ez-Sanagustin","given":"Mar","non-dropping-pa= rticle":"","parse-names":false,"suffix":= ""}],"container-title":"IEEE Transactions on Learning Technologies","id":"ITEM-1&quo= t;,"issue":"4","issued":{"date-parts&quo= t;:[["2020","10","1"]]},"page":&quo= t;676-688","publisher":"Institute of Electrical and Electronics Engineers Inc.","title":"= Characterizing Learners' Engagement in MOOCs: An Observational Case Study Using the NoteMyProgress Tool for Supporting Self-Regulation","type":"article-journal","vo= lume":"13"},"uris":["http://www.mendeley.com/= documents/?uuid=3D8c21d089-efde-38c2-9533-cdf2489c8eb8","http://w= ww.mendeley.com/documents/?uuid=3De253f5a2-6d12-4784-aaa9-da23df33fde1"= ;]}],"mendeley":{"formattedCitation":"(Perez-Alvar= ez et al., 2020)","plainTextFormattedCitation":"(Perez-Alv= arez et al., 2020)","previouslyFormattedCitation":"(Perez-Alvarez et al., 2020)"},"properties":{"noteIndex":0},"sc= hema":"https://github.com/citation-style-language/schema/raw/mast= er/csl-citation.json"}(Perez-A= lvarez et al., 2020). Es así, que se evidencia el interés de estudiantes y docentes por (iv) conocer acerca = de la frecuencia con la que son accedidas las lecturas, videos y problemas dentro= de un curso. Además, de analizar la (v) interacción de los estudiantes entre sí dentro de los foros del curso. Otro punto importante es el (vi) tiempo invertido en las actividades, así como también el rendimiento, el cual es evidenciado a través de la (vii) cantidad de re= cursos que completan dentro de la plataforma.

 

Como siguiente= paso, para llegar a comprender el comportamiento de los estudiantes dentro de los SPOC, es importante conocer acerca de sus secuencias de interacción durante= el proceso de aprendizaje. Vermunt en su estudio <= /span>(Vermunt= & Donche, 2017), explica que, una secuencia de aprendiza= je se conceptualiza como un conjunto coherente de actividades (secuencias o conju= nto de interacciones) que suelen emplear los estudiantes. Estas secuencias pued= en ser capturadas a través del análisis de logs de datos que contengan las interacciones de los estudiantes con los recursos digitales del curso. Para esto, el uso de técnicas de minería de procesos sobre los datos educativos permiten construir de manera visual los caminos o trayectorias utilizadas p= or los estudiantes mientras siguen el curso (Borouje= ni & Dillenbourg, 2018). Para lograrlo, se utilizó los datos que= se almacenan en Open edX de un SPOC y se extrajo u= n log de eventos que fue procesado empleando el software Disco (ver Tabla 1). Como resultado se obtuvo un modelo de procesos (Figura 2= ) que representa las principales trayecto= rias seguidas por los estudiantes en un SPOC.

 

 

Tabla <= /span>1=

Columnas del log de eventos ingre= sados en Disco

COLUMNA

DESCRIPCIÓN

user_id

Hace referencia al id del estudiante que está generando el evento.=

course_id

Id del curso en el que está el estudiante.

block_id

Correspondiente al id del bloque que está analizando en ese moment= o.

event_type

variable creada para poder identificar el tipo de evento que se es= tá analizando.

time

La fecha y hora que se desencadeno el evento.

 

Figura = 2=

Modelo de procesos de los estudia= ntes en un SPOC

=

 

Por medio del = análisis del modelo de procesos generado se identificaron las secuencias de aprendiz= aje principales de todos los estudiantes (ver Figura 2). Como resultado se pudo observar que los estudiantes: (1) inician con las video-lecturas del curso,= las completan, luego se mantienen en un bucle de interacciones entre completar = los problemas propuestos y regresar a visualizar problemas antes desarrollados, para luego finalizar su sesión de trabajo; (2) en la segunda secuencia que = se puede identificar que los estudiantes inician completando las video-lectura= s y luego regresan a consultarlas antes de avanzar a completar los problemas propuest= os.

 

 Estas secuencias de aprendizaje son las = más comunes que realizan los estudiantes y se puede observar también que los estudiantes no revisan todas las video-lecturas y tampoco desarrollan las evaluaciones propuestas (lo cual es un comportamiento no esperado, pues las evaluaciones se deben de realizar una vez que terminen las video-lecturas) debido a esto es que se pudo observar que los estudiantes repiten los probl= emas de las evaluaciones.

 

Una vez identi= ficadas estas secuencias de interacciones de los estudiantes, sobre los modelos de procesos obtenidos se empleó la técnica de clustering<= /span> o agrupamiento para clasificar a los estudiantes en grupos dependiendo sus secuencias o trayectorias de aprendizaje. Como resultado se observaron tres grupos que caracterizan el comportamiento de los estudiantes en un SPOC. En= el primer grupo, están los estudiantes que trabajaro= n solo con las evaluaciones propuestas y no tuvieron interacciones con las video-lecturas del curso u otro tipo de actividades. Este patrón concuerda = con el identificado en el trabajo de (Maldonado-Mahauad et al., 2018), donde denomina a este tipo de estudiantes como estratégicos u orientados a = los objetivos personales. Este tipo de estudiantes tienden a centrar sus esfuerzos en pasar las evaluacion= es a fin de certificar o probar sus conocimientos.

 

En el segundo = grupo,  se observaron estudiantes exploradores <= /span>(Maldona= do-Mahauad et al., 2018). Este grupo de estudiantes se caracteriz= an por que sus interacciones son muy cortas (de pocos segundos, por ende, las sesi= ones de estudio son cortas), no completan las evaluaciones ni problemas propuest= os, y las interacciones con los recursos del curso son erráticas. Finalmente, e= n el tercer grupo, se identificó a los estudiantes “comprensivos” (Maldona= do-Mahauad et al., 2018). Este grupo se encuentra compuesto por l= os que realizan todas las video-lecturas y problemas propuestos, son los que mayor cantidad de interacciones tienen con los recursos del curso y siguen la trayectoria instruccional propuesta por el curso. Además, estos estudiantes exhiben un mayor nivel de compromiso para terminar con éxito el curso.=

 

Como resultado= de esta primera etapa, se extrajeron las características que tanto docentes como es= tudiantes indicaron debe tener un dashboard de visualizac= iones. Por otro lado, también se ha extraído de los datos, las secuencias de aprendizaje de los estudiantes en un SPOC, logrando develar los tipos de interacciones más comunes y que permiten clasificar a los estudiantes en tr= es grupos.

 

Identificación de variables

En esta segund= a etapa se identificaron indicadores y variables relacionados al éxito académico de estudiantes en SPOC, con el objetivo de incorporarlos en el diseño de las visualizaciones del componente XBlock. L= os indicadores y variables seleccionadas que se muestran en la Tabla 2, resultaron del análisis de la etapa 1. = Estos indicadores y variables fueron mapeados con los indicadores que se proveen = en la base de datos de Open edX, con el fin de pod= er entender si era posible extraerlos de manera directa o se requerían cálculo= s o integraciones de datos intermedios.

 

Tabla <= /span>2=

Indicadores y variables seleccion= adas para incluir en el diseño del XBlock

VARIABLE

INDICADOR

DESCRIPCIÓN

Frecuencia de las actividades

interaction_foros

Cantidad de veces que un estudiante interactúa en los foros

completed_lectures

Cantidad de lecturas completadas

videos_complete

Cantidad de videos completados

complete_problem

Cantidad de problemas completados

Secuencias y patrones de aprendizaje

repeated_lectures

Cantidad de veces que un estudiante repite una lectura<= /span>

repeat_videos

Cantidad de veces que un estudiante repite un video

repeat_problem

Cantidad de veces que un estudiante repite un problema<= /span>

num_play_videos

Cantidad de veces que se ha reproducido un video=

num_pause_videos

Cantidad de veces que se ha pausado un video

num_stop_videos

Cantidad de veces que se ha detenido un video

num_load_videos

Cantidad de veces que se ha accedido a un video<= /span>

num_fast_forward_videos

Cantidad de veces que se ha adelantado un video<= /span>

num_rewind_videos

Cantidad de veces que se ha retrocedido un video=

num_accessed_resources

Cantidad de recursos accedidos por un estudiante=

Rendimiento a través del tiempo

week_id

Número de la semana en la que se desarrollan las actividades de un estudiante

start_date_week

Fecha en la que comienza un periodo de actividades de 7 días<= /o:p>

end_date_week

Fecha en la que finaliza un periodo de actividades de 7 días<= /o:p>

num_sessions

Cantidad de veces que un estudiante se ha conectado a la plataform= a.

sum_time_sessions

Tiempo invertido del estudiante en la plataforma=

 

Planificación y Diseño

En esta tercer= a etapa, como primer paso, se analizaron dashboards desarrollados por diferentes proyectos. Por ejemplo, ANALISE y OXALIC (Khalil = & Belokrys, 2020; Ruipérez-Valiente et al., 2017). Estos proyectos han desarrollado sus pr= opios dashboards para ser utilizados en la plataforma Open = edX. Estos proyectos se enfocan en el proceso de aprendizaje de los estudiantes dentro de un curso, para luego mostrar esta información a los profesores mediante visualizaciones. Estas permiten (1) visualizar métricas acerca de la inscripción de los estudiantes; (2) el seguimiento de las actividades de los estudiantes; y (3) visualizar el cont= eo de las interacciones de los estudiantes con el contenido del curso.

 

Como segundo p= aso, se hizo uso de la guía para la elección de visualizaciones para dashboards de aprendizaje denominada Linking dashboard design and data visualization concepts (Sedraky= an et al., 2019). Esta guía presenta una metodología que = se enfoca en los siguientes aspectos al momento de elegir las visualizaciones más adecuadas = al construir un dashboard: (a) objetivos y necesidades de los interesados= ; (b) fuente de los datos; (c) retroalimentación y autorregulación del aprendizaj= e; y (4) facilidad para mostrar el progreso del proceso de aprendizaje.

 

Como resultado, tomando como base el análisis exploratorio de las interacciones de los estudiantes realizado en la etapa 1 y siguiendo la guía para la elección de visualizaciones, se determinó que estudiantes y docentes tienen la necesidad de: (a) conocer sobre de la frecuencia con la que se interactúa un estudiante con los recursos del curso; (b) requieren evidenci= ar el tiempo invertido en semanas; (c) identificar las brechas entre el estado= actual y el estado deseado de los estudiantes con respecto a la comparación con el promedio de sus compañeros dentro de un curso (Schunk = & Zimmerman, 2012); y (d) facilitar la comparación del proc= eso de aprendizaje en periodos de tiempo uniforme, comúnmente dividido en semanas = (Ramesh = et al., 2014). Esto ha servido para implementar diferentes visualizaciones que se describen en la siguiente etapa.

 

Implementación

En esta cuarta= etapa, se implementa la arquitectura del XBlock= junto con las visualizaciones para docentes y estudiantes. Las tecnologías emplea= das en el XBlock para la plataforma Open edX son las mismas que se usa en las aplicaciones web, donde se tienen múltiples componentes de manera independiente para ser mostrados en una sola página web (edX Inc= , 2021). Las aplicaciones de tipo Web que implem= enten XBlock API son consideradas XBlocks en entorno de ejecución (edX Inc= , 2021). Estas aplicaciones pueden componerse de= una o varias páginas Web. Además cuentan con una capa de almacenamiento para mant= ener su estado, controladores para procesar las acciones del usuario y además cuentan con vistas para poder ser renderizadas (edX Inc= , 2021).

 

La representac= ión gráfica de la arquitectura propuesta con todas las capas antes mencionadas = se muestra en la Figura 3. La primera capa de la arquitectura prop= uesta para el XBlock denominado XLEA (XBlock for LEarning Analytics) presenta la parte visual. En esta pr= imera capa es donde los estudiantes y los profesores interactúan con las visualizaciones, y está compuesta por HTML, CSS y Java= script, esta aplicación integra XBlock API para = poder acceder a la configuración con la que cuenta la plataforma de Open edX.

 

Figura = 3=

Arquitectura propuesta para el XBlock

=

 

La segunda cap= a de la arquitectura facilita la comunicación con el código base de la plataforma de Open edX por medio del = XBlock Runtime. En esta capa es donde el LMS y CMS req= uiere que los XBlocks tengan las siguientes propiedades: (1) tener un método de la vista denominado student_view que en el caso del LMS, esta se pueda interactuar, mientras que, en el CMS = el instructor pueda configurar; (2) tener una propiedad llamada has_score con un valor de True si el XBlock permite realizar calificaciones, caso contrario este valor deberá ser configurado como False. Finalmente, la terc= era capa de la arquitectura propuesta es la comunicación con los sistemas de persistencia de base de datos para recuperar la información almacenada.

 

La comunicació= n con las bases de datos tanto de MySQL como de MongoDB se realiza por medio del = XBlock Runtime que= permite acceder en tiempo de ejecución a los datos almacenados y poder comunicar con las capas superiores. En esta capa es donde el XBlo= ck puede acceder a la información procesada de la interacción de los estudiant= es, pues previamente se ha almacenado en la base de datos de MySQL, empleando el script desarrollado.

 

Empleando la arquitectura antes mencionada, en el XBlock a desarrollar se incorporan funcionalidades adicionales a las ya existentes dentro de la plataforma Open edX. De esta manera tanto los estudiantes y docentes pueden tomar decisiones informadas a base = de la información mostrada en el dashboard. Las funcionalidades que brinda el componente desarrollado son (1) Vista general= de las interacciones de los estudiantes con los recursos que se encuentran den= tro del curso; (2) Medir y comparar los logros conseguidos de un estudiante con= la media del curso; y (3) Comparación del tiempo dedicado por un estudiante co= n el tiempo promedio que ocupan los estudiantes en una semana de actividades en = la plataforma.

 

Como resultado= , las visualizaciones implementadas en el XBlock cuentan con dos tipos de vistas, la del estudiante y la del docente. Las visualizaciones se diseñaron a base de los objetivos y requerimientos tomando en cuenta los indicadores obtenidos en las etapas anteriores. En la vista d= el estudiante se muestran gráficas que presentan el rendimiento general de los estudiantes por semanas. Los indicadores que se utilizaron para el diseño de este gráfico fueron la cantidad de recursos a los que ha accedido el estudi= ante a lo largo de una semana, es decir, lecturas, videos, foros y exámenes, tal como se lo muestra en la Figura 4.

 

Figura = 4=

Rendimiento general por semanas. = Vista del estudiante

 

Adicionalmente= en la Figura 5 se compara el rendimiento individual del estudiante contra el rendimiento del curso en promedio, con el fin de dar a= los estudiantes información acerca del estado de su proceso de aprendizaje. Y p= or último se muestran indicadores relacionados a la cantidad de recursos completados por el estudiante a lo largo del curso en la Figura 6.

 

 

 

 

 

 

 

 

Figura = 5=

Tiempo invertido en el curso por = semanas. Vista del estudiante

 

Figura = 6=

Recursos completados a lo largo d= el curso. Vista del estudiante

 

Para la vista = del docente las visualizaciones se diseñaron a base del promedio de las interacciones de todos los estudiantes del curso. En la Figura 7 se muestran los recursos completados por semanas de todos los estudiantes del curso, separados en lecturas, videos, foros y problemas.

Figura = 7=

Recursos completados por semanas = de todos los estudiantes del curso. Vista del docente

=

 

Las sesiones en promedio por semana y su duración, así también la cantidad de recursos accedidos en promedio por semana de todos los estudiantes del curso se mues= tra en la Figura 8.

 

Figura = 8=

Sesi= ones promedio por semana y recursos accedidos en promedio por semana. Vista del docente

 <= /p>


Finalmente, en= la Figura 9 se muestra una tabla con las estadísticas individuales de cada estudiante del curso. Se incluyen indicadores como el número de recursos accedidos, lecturas, videos, problemas, foros, número de sesiones y el tiempo invertido en el curso.

 

Figura = 9=

Estadísticas de los indicadores d= e cada estudiante del curso. Vista del docente

=

 

 

 

 

Evaluación

Como última et= apa de la metodología LATUX empleada en este trabajo, se realizó la evaluación del componente XBlock. Para la evaluación se= hizo uso del cuestionario Evaluation Frame= work for Learning Analytics (EFLA) (Scheffe= l et al., 2017). Mientras que para la evaluación de la experiencia de usuario al usar el dashboard, se utilizó el cuestionario User Experience Questionarie (UEQ) (Laugwit= z et al., 2008).

 

Participant= es

La evaluación = de las visualizaciones incorporadas en el XBlock fue realizada por estudiantes y profesores de la Universidad de Cuenca (muestre= o a conveniencia). Los participantes tuvieron acceso completo a todas las visualizaciones, tomando en cuenta su rol dentro de la plataforma, se proporcionará el cuestionario respectivo. La cantidad de participantes sele= ccionados son 14, de los cuales 10 son estudiantes; y los 4 profesores que participar= on en la evaluación de igual manera han tenido experiencia en impartir cursos MOOC.

 

Instrumento= s

Se seleccionó = el cuestionario de evaluación EFLA, cuyo objetivo es permitir entender desde la perspectiva del estudiante y del docente la significancia de las visualizaciones que se presentan para la toma de decisiones. Este cuestiona= rio cuenta con dos versiones una para profesores (https://forms.gle/1uMg9FSHj4jTcicYA) y otra para estudiantes (https://forms.gle/P18rwhyXLX45vC3Y6).

 

Las preguntas = que se deben de responder están en una escala del 1 al 10, donde el 10 es el valor= de mayor importancia mientras que el valor 1 es de menor importancia. Este cuestionario cuenta con tres dimensiones las mismas que se muestran en la <= /span>Tabla 3 y para obtener cada uno de los valores d= e cada dimensión se tiene que usar la ecuación (1).

 

Tabla <= /span>3=

Dimensiones de EFLA

DIMENSIÓN

DESCRIPCIÓN

INDICADORES

Datos

Esta variable está relacionada al acceso y la presentación de los datos

Transparencia

Manipulación

Concientización y Reflexión

Esta variable está relacionada a la proyección a futuro del esta= do de aprendizaje de los estudiantes

Comparabilidad

Recomendación

Clasificación de actividades

Comportamiento

Impacto

Esta variable está relacionada a la motivación que sienten los usuarios al usar la herramienta

Efectividad

Eficiencia

 

                                         = (1)

 

El otro cuesti= onario empleado fue el UEQ que es ampliamente usado para medir la impresión subjet= iva que tienen los usuarios sobre la calidad y usabilidad en los productos de software (Schrepp= et al., 2017). El cuestionario empleado (https://forms.gle/TDfnch3QisUct2Ce9)  tiene una escala de Likert para medir l= as reacciones, actitudes y comportamientos de las personas donde 1 es totalmen= te en desacuerdo y 5 totalmente de acuerdo; se ha seleccionado esta escala deb= ido a que a diferencia de las preguntas de si/no está permite tener una mayor libertad en calificar las respuestas (Nemoto = & Beglar, 2014). Los factores empleados para medir la experiencia que tiene el usuario con el producto de software se muestran a continuación:

 

·         A= tractivo: Impresión general del producto. ¿A los usuarios les gusta o disgusta el producto?

·         C= laridad: ¿Es fácil familiarizarse con el product= o? ¿Es fácil aprender a usar el producto?

·         E= ficiencia: ¿Pueden los usuarios resolver sus tarea= s sin esfuerzo innecesario?

·         C= onfianza: ¿Se siente el usuario en control de la interacción?

·         E= stímulo: ¿Es emocionante y motivador usar el pro= ducto?

·         N= ovedad: ¿Es el producto innovador y creativo? ¿= El producto capta el interés de los usuarios?

 

Resultados =

Los resultados= de la evaluación de los estudiantes y profesores empleando el cuestionario EFLA se muestran en la Tabla 4. En esta tabla se presentan los resultad= os al emplear la formula (1) en cada una de las dimensiones analizadas tanto para= los profesores como para los estudiantes.

 

Tabla <= /span>4=

Resultados de la evaluación con el cuestionario EFLA

DIMENSIÓN

EFLA SCORE DE ESTUDIANTES

EFLA SCORE DE PROFESORES

Datos

79.44

80.55

Concientización y Reflexión

81.67

77.08

Impacto

77.78

77.78

 

Enfocándonos en los resultados de EFLA aplicado a estudiantes (n=3D 14), las dimensiones de evaluación mantienen un puntaje p= or arriba de los 77/100 puntos (77%), esto demuestra que la herramienta XBlock pr= esenta de manera sencilla y clara los datos en sus visualizaciones, de la misma fo= rma, los estudiantes se sienten motivados a reflexionar acerca de su comportamie= nto y proceso de aprendizaje. De igual manera los resultad= os del EFLA aplicado a docentes, se observa que las dimensiones de evaluación mantienen valores por encima de los 77/100 puntos (77%), destacando con 80 puntos la dimensión de datos. Este resultado indica que los docentes entien= den de qué manera se recopilan y muestran los datos acerca de las interacciones= de los estudiantes, permitiéndoles reflexionar acerca de su método de enseñanza y en cómo pueden mejorar en el proceso.

 

Los resultados= de la evaluación empleando el cuestionario UEQ (Tabla 5<= /span>)para medir la experiencia del usuario mu= estra que la mitad de los participantes (n=3D 14) están de acuerdo que las visualizaciones les han parecido atractivas. El 80% de los participantes es= tá de acuerdo que el dashboard presenta las visualiza= ciones de forma ordenada y fácil de entender por lo que no tuvieron la necesidad de aprender alguna herramienta para su uso. El 70% de los participantes están = de acuerdo que el dashboard les permite tener un c= ontrol sobre su proceso de aprendizaje y en igual medida de participantes indican haber tenido interés luego de usarla. El 60% de los participantes están dispuestos a seguir usando la herramienta mientras que el 40% restante tenía dudas, esto se puede deber al margen de mejora que existe en las visualizaciones del dashboard según la retroalimentación de los participantes.

 

Tabla <= /span>5=

Resultados de la evaluación con el cuestionario UEQ

PREGUNTA

# PERSONAS TOTALMENTE EN DESACUERDO=

# PERSONAS EN DESACUERDO

# PERSONAS NI DE ACUE= RDO, NI EN DESACUERDO

# PERSONAS DE ACUERDO<= /b>

# PERSONAS TOTALMENTE DE ACUERDO

Me pareció atractiva la herramienta

0

0

1

8

5

Las figuras se encuentran organizadas de una man= era fácil de entender

0

0

2

10

2

Tuve que aprender otras cosas para usar la herramienta

9

3

2

0

0

Esta herramienta me ayuda a mí a tener un control sobre mi aprendizaje

0

0

4

9

1

La herramienta captó mi interés

0

0

4

8

2

La herramienta motiva a utilizarla de nuevo=

0

0

5

7

2

&n= bsp;

Discusión de los Resultados

En otros trabajos, como es el caso de ANA= LIZE, los autores obtuvieron resultados positivos luego de evaluar su herramienta= de analíticas de aprendizaje con una escala de usabilidad del sistema (SUS) de 78/100 puntos (Ruipérez-Valiente et al= ., 2017). Estos resultados evidencian la necesida= d de los estudiantes por utilizar herramientas que den soporte a la toma de decisiones a través de las LA, lo cual se contrasta con los resultados de nuestro trabajo, donde el 80% de los participantes afirman la importancia y necesidad del uso de este tipo de herramientas.

 

Adicionalmente, se añadió una sección de sugerencias en el cuestionario UEQ, del cual se pudieron identificar mejoras para nuestro dashboard de analíticas de aprendi= zaje. Estas mejoras pueden resumirse en los siguientes puntos: (1) Debería incluirse una descripción de las funcionalidades de la herramienta, de mane= ra que quede claro lo que se puede obtener de la misma; (2) Se podría añadir m= ás información para contextualizar un escenario donde se explique los recursos accedidos por los estudiantes no sólo son videos e información, sino también cuando el estudiante aprueba o reprueba un examen. De esta forma, el docente puede percibir los resultados como una retroalimentación de qué temas se pu= ede reforzar para mejorar el proceso de enseñanza-aprendizaje; (3) con respecto= a pequeños detalles de la interfaz, la tabla de estadísticas debería indicar claramente las unidades de las columnas, por ejemplo, cantidad, tiempo, etc= . De igual manera, se debería poder acceder a la información de semanas previas.= (4) Pequeños cambios en los tipos de letras, tamaños, colores, cabeceras de las tablas, que podrían mejorar la experiencia de usuario.

 

 

 

Conclusiones

En este trabaj= o se ha presentado el desarrollo de un componente XBlock que implementa, por un lado, visualizaciones para estudiantes a fin de dar cuenta de su proceso de aprendizaje; y, por otro lado, visualizaciones para docentes a fin de que puedan monitorear, hacer seguimiento y retroalimentar= a los estudiantes en un SPOC. Para esto se ha utilizado la metodología de 5 etapas propuesta por LATUX (Learning Awareness<= /span> Tool – User eXperience) (Martinez-Maldonado et a= l., 2016) donde (1) se parte del desarrollo de un análisis exploratorio sobre el comportami= ento de los estudiantes en un SPOC para (2) identificar variables y secuencias de aprendizaje comunes; (3) se propone un diseño de un da= shboard de visualizaciones para profesores y estudiantes a partir de las variables y secuencias detectadas; (4) se implementa el dashboard<= /span> bajo la forma de un XBlock para la plata= forma abierta Open edX que permita visualizar el comportamiento de los estudiantes; y (5) finalmente se hace una evaluación local para evaluar la significancia de las visualizaciones que se presentan para la toma de decisiones; y evaluar la usabilidad de las visualizaciones desarrolladas en= el XBlock. Como resultado se evidencia un alto gr= ado de aceptación y conformidad en el uso de las visualizaciones para la toma de decisiones por parte de estudiantes y docentes al utilizar el XBlock, también el interés por hacer seguimien= to de su comportamiento a lo largo de las semanas mediante las visualizaciones de= l dashboard.

 

Como resultado= de la evaluación, los participantes, en sus respuestas evidenciaron el interés po= r el uso de esta herramienta que brinde soporte a los estudiantes y docentes de = un curso. Cerca del 80% de los participantes perciben al = XBlock como un estímulo positivo para redirigir su comportamiento y estrategias de aprendizaje o enseñanza hacia el éxito académico dentro del curso. Sin emba= rgo, existen participantes que aseguran que el dashboard puede mejorar su experiencia de usuario añadiendo descripciones y elementos= que guíen a aquellos que no han tenido experiencia previa en el manejo de dashboards de LA. En la retroalimentación brindada po= r los participantes, un 20% expresa que los gráficos deberían cambiar ciertos detalles como colores, tamaños y tipos de letras. Tales observaciones están relacionadas con la experiencia de usuario, mientras que, cerca del 80% est= án de acuerdo en qué el dashboard cumple con su ob= jetivo y les permite conocer acerca de su estado actual de aprendizaje o enseñanza= con el fin de modificar sus estrategias y comportamiento para alcanzar el éxito académico.

 

A pesar de est= o, existe un alto grado de aceptación y conformidad por parte de los participantes. La mayoría se enfoca en aquellas visualizaciones que permiten tener una comprensión del comportamiento histórico por semanas de las diferentes actividades que desempeñan dentro del curso, por ejemplo, videos, lecturas y problemas. Finalmente, en este trabajo se ha demostrado que es posible crear un XBlock dentro de la pla= taforma Open edX que, partiendo de las necesidades de l= os involucrados, permitan tanto a estudiantes y profesores mejorar su proceso = de aprendizaje y enseñanza dentro de un SPOC. Adicionalmente, se ha puesto en evidencia la necesidad de los estudiantes por dashboar= ds como el desarrollado en este XBlock que = les ofrezcan visualizaciones sobre su comportamiento de aprendizaje que antes s= olo estaban disponibles para los docentes.

 

El alcance del= estudio está limitado al análisis del comportamiento de los estudiantes a través de visualizaciones que incorporen indicadores de éxito estudiantil en cursos d= e la plataforma Open edX. Los resultados obtenidos e= n este estudio pueden abrir nuevas líneas de investigación centrándose en el mejoramiento y rediseño de material educativo en cursos ofertados en esta plataforma. Otra línea de investigación podría centrarse en el procesamient= o de los logs de eventos que generan los estudiantes en la plataforma Open edX, ya que actualmente resulta complicado procesar e= sta información debido a la falta de una estructura concisa y estable de los da= tos en las diferentes versiones que maneja la plataforma. Finalmente, existe ma= rgen de mejora relacionado al diseño de visualizaciones de LA dentro de los MOOC= , el cual es un área sujeta a cambios e innovaciones.

 

Agradecimientos

Este trabajo t= iene el apoyo académico del proyecto de investigación “Analítica del aprendizaje pa= ra el estudio de estrategias de aprendizaje autorregulado en un contexto de aprendizaje híbrido”, del director del proyecto Ing. Jorge Maldonado Mahauad, PhD. docente de la Facultad de Ingeniería de= la Universidad de Cuenca. Adicionalmente, se agradece al Vicerrectorado de Investigación de la Universidad de Cuenca por proveer los datos de estudian= tes de 4 MOOC que se utilizaron para este estudio. También se agradece a la Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia (CEDIA) por facilitar el uso de infraestructura necesaria para desplegar un ambiente de producción del dashboard XLEA.=

 

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