6 research outputs found
Do optional activities matter in virtual learning environments?
Virtual Learning Environments (VLEs) provide students with activi-ties to improve their learning (e.g., reading texts, watching videos or solving exercises). But VLEs usually also provide optional activities (e.g., changing an avatar profile or setting goals). Some of these have a connection with the learn-ing process, but are not directly devoted to learning concepts (e.g., setting goals). Few works have dealt with the use of optional activities and the relation-ships between these activities and other metrics in VLEs. This paper analyzes the use of optional activities at different levels in a specific case study with 291 students from three courses (physics, chemistry and mathematics) using the Khan Academy platform. The level of use of the different types of optional ac-tivities is analyzed and compared to that of learning activities. In addition, the relationship between the usage of optional activities and different student be-haviors and learning metrics is presented
Evaluation of a Learning Analytics Application for Open edX Platform
Massive open online courses (MOOCs) have recently emerged as a revolution in education. Due to the huge amount of users, it is difficult for teachers to provide personalized instruction. Learning analytics computer applications have emerged as a solution. At present, MOOC platforms provide low support for learning analytics visualizations, and a challenge is to provide useful and effective visualization applications about the learning process. At this paper we review the learning analytics functionality of Open edX and make an overview of our learning analytics application ANALYSE. We present a usability and effectiveness evaluation of ANALYSE tool with 40 students taking a Design of Telematics Applications course. The survey obtained very positive results in a system usability scale (SUS) questionnaire (78.44/100) in terms of the usefulness of visualizations (3.68/5) and the effectiveness ratio (92/100) of the actions required for the respondents. Therefore, we can conclude that the implemented learning analytics application is usable and effective.Acknowledgements: This work has been supported by the "eMadrid" project (Regional Government of Madrid) under grant S2013/ICE-2715, the "RESET" project (Ministry of Economy and Competiveness) under grant RESET TIN2014-53199-C3-1-R and the European Erasmus+ SHEILA project under grant 562080-EPP-1-2015-BE-EPPKA3-PI-FORWARD
The virtual learning environment analyses through historical and cultural focus
La enseñanza a distancia en la actualidad es una forma de organización de la
enseñanza con un fuerte basamento tecnológico. Uno de los soportes
tecnológicos para la enseñanza a distancia en la actualidad lo son las
plataformas educativas de las cuales exi
sten una gran variedad. Es por ello
que el siguiente trabajo se propone como problema: ¿Cómo elaborar una
plataforma educativa desarrolladora en los estudiantes de la enseñanza a
distancia? Teniendo en cuenta los elementos planteados se propone como
objeti
vo elaborar un conjunto de fundamentos para elaborar plataformas
educativas para conducir el aprendizaje de los estudiantes desde una
concepción del enfoque histórico cultural.Distance learning is now a form of organization of teaching with a strong
technological base. One of the technological support for distance learning
today are educational platforms of which there ar
e many. That is why the
following work is proposed as a problem: How to develop an educational
platform developer students in distance learning? Considering the matters
presented as objective develop a set of fundamentals to develop educational
platforms t
o drive student learning from a conception of cultural historical
approach
Analyzing the behavior of students regarding learning activities, badges, and academic dishonesty in MOOC environment
Mención Internacional en el título de doctorThe ‘big data’ scene has brought new improvement opportunities to most products and services,
including education. Web-based learning has become very widespread over the last decade,
which in conjunction with the Massive Open Online Course (MOOC) phenomenon, it has enabled
the collection of large and rich data samples regarding the interaction of students with these educational
online environments.
We have detected different areas in the literature that still need improvement and more research
studies. Particularly, in the context of MOOCs and Small Private Online Courses (SPOCs),
where we focus our data analysis on the platforms Khan Academy, Open edX and Coursera. More
specifically, we are going to work towards learning analytics visualization dashboards, carrying
out an evaluation of these visual analytics tools. Additionally, we will delve into the activity and
behavior of students with regular and optional activities, badges and their online academically
dishonest conduct. The analysis of activity and behavior of students is divided first in exploratory
analysis providing descriptive and inferential statistics, like correlations and group comparisons,
as well as numerous visualizations that facilitate conveying understandable information. Second,
we apply clustering analysis to find different profiles of students for different purposes e.g., to analyze
potential adaptation of learning experiences and pedagogical implications. Third, we also
provide three machine learning models, two of them to predict learning outcomes (learning gains
and certificate accomplishment) and one to classify submissions as illicit or not. We also use these
models to discuss about the importance of variables.
Finally, we discuss our results in terms of the motivation of students, student profiling,
instructional design, potential actuators and the evaluation of visual analytics dashboards
providing different recommendations to improve future educational experiments.Las novedades en torno al ‘big data’ han traído nuevas oportunidades de mejorar la mayoría
de productos y servicios, incluyendo la educación. El aprendizaje mediante tecnologías web se
ha extendido mucho durante la última década, que conjuntamente con el fenómeno de los cursos
abiertos masivos en línea (MOOCs), ha permitido que se recojan grandes y ricas muestras de
datos sobre la interacción de los estudiantes con estos entornos virtuales de aprendizaje.
Nosotros hemos detectado diferentes áreas en la literatura que aún necesitan de mejoras y del
desarrollo de más estudios, específicamente en el contexto de MOOCs y cursos privados pequeños
en línea (SPOCs). En la tesis nos hemos enfocado en el análisis de datos en las plataformas Khan
Academy, Open edX y Coursera. Más específicamente, vamos a trabajar en interfaces de visualizaciones
de analítica de aprendizaje, llevando a cabo la evaluación de estas herramientas
de analítica visual. Además, profundizaremos en la actividad y el comportamiento de los estudiantes
con actividades comunes y opcionales, medallas y sus conductas en torno a la deshonestidad
académica. Este análisis de actividad y comportamiento comienza primero con análisis
exploratorio proporcionando variables descriptivas y de inferencia estadística, como correlaciones
y comparaciones entre grupos, así como numerosas visualizaciones que facilitan la transmisión
de información inteligible. En segundo lugar aplicaremos técnicas de agrupamiento para encontrar
distintos perfiles de estudiantes con diferentes propósitos, como por ejemplo para analizar
posibles adaptaciones de experiencias educativas y sus implicaciones pedagógicas. También proporcionamos
tres modelos de aprendizaje máquina, dos de ellos que predicen resultados finales
de aprendizaje (ganancias de aprendizaje y la consecución de certificados de terminación) y uno
para clasificar que ejercicios han sido entregados de forma deshonesta. También usaremos estos
tres modelos para analizar la importancia de las variables.
Finalmente, discutimos todos los resultados en términos de la motivación de los estudiantes,
diferentes perfiles de estudiante, diseño instruccional, posibles sistemas actuadores, así como la
evaluación de interfaces de analítica visual, proporcionando recomendaciones que pueden ayudar
a mejorar futuras experiencias educacionales.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Davinia Hernández Leo.- Secretario: Luis Sánchez Fernández.- Vocal: Adolfo Ruiz Callej
Do optional activities matter in virtual learning environments?
Virtual Learning Environments (VLEs) provide studentts with activities to improve their learning (e.g., reading texts, watching videos or solving exercises). But VLEs usually also provide optional activities (e.g., changing an avatar profile or setting goals). Some of these have a connection with the learning process, but are not directly devoted to learning concepts (e.g., setting goals). Few works have dealt with the use of optional activities and the relationships between these activities and other metrics in VLEs. This paper analyzes the use of optional activities at different levels in a specific case study with 291 students from three courses (physics, chemistry and mathematics) using the Khan Academy platform. The level of use of the different types of optional activities is analyzed and compared to that of learning activities. In addition, the relationship between the usage of optional activities and different student behaviors and learning metrics is presented