413 research outputs found
Using Multiple Accounts for Harvesting Solutions in MOOCs
The study presented in this paper deals with copying answers in MOOCs. Our findings show that a significant fraction of the certificate earners in the course that we studied have used what we call harvesting accounts to find correct answers that they later submitted in their main account, the account for which they earned a certificate. In total, ~2.5% of the users who earned a certificate in the course obtained the majority of their points by using this method, and ~10% of them used it to some extent. This paper has two main goals. The first is to define the phenomenon and demonstrate its severity. The second is characterizing key factors within the course that affect it, and suggesting possible remedies that are likely to decrease the amount of cheating. The immediate implication of this study is to MOOCs. However, we believe that the results generalize beyond MOOCs, since this strategy can be used in any learning environments that do not identify all registrants.Madrid (Spain: Region) (eMadrid Grant S2013/ICE-2715)Spain. Ministerio de Economia y Competitividad (Grant RESET TIN2014-53199-C3-1-R
Identifying cheating users in online courses
Máster Universitario en en Investigación e Innovación en Inteligencia Computacional y Sistemas InteractivosStudents interact with online courses mainly in two ways: by reviewing the course materials
and by solving exercises. However, there are cases in which student behaviour differs and tends
to become more focused on solving exercises without looking at course materials. This type
of interaction could be an indicative of unethical behavior, such as students who collaborate
by sharing answers with one another or fake accounts that are used by students to obtain the
correct answers for exercises.
In this work, we propose several metrics to identify these two types of cheating based on cooccurring
events and measures of interaction with the course. From the pool of accounts in
the course, the pairs of accounts that solve exercises very close in time are considered to be
potential collaborating accounts.
The proposed metrics are computed for these pairs of accounts and K-means clustering is used
to separate pairs of real students who collaborate with respect to students who use fake accounts
to harvest the correct answers to exercises. A generalization accuracy over 95% to classify these
types of cheating is achieved by using a Support Vector Machine (SVM
Evaluating the Robustness of Learning Analytics Results Against Fake Learners
Massive Open Online Courses (MOOCs) collect large amounts of rich data. A primary objective of Learning Analytics (LA) research is studying these data in order to improve the pedagogy of interactive
learning environments. Most studies make the underlying assumption that the data represent truthful and honest learning activity. However, previous studies showed that MOOCs can have large cohorts of users that
break this assumption and achieve high performance through behaviors such as Cheating Using Multiple Accounts or unauthorized collaboration, and we therefore denote them fake learners. Because of their aberrant
behavior, fake learners can bias the results of Learning Analytics (LA) models. The goal of this study is to evaluate the robustness of LA results when the data contain a considerable number of fake learners. Our
methodology follows the rationale of ‘replication research’. We challenge the results reported in a well-known, and one of the first LA/PedagogicEfficacy MOOC papers, by replicating its results with and without the fake learners (identified using machine learning algorithms). The results show that fake learners exhibit very different behavior compared to true learners. However, even though they are a significant portion of the student
population (∼15%), their effect on the results is not dramatic (does not change trends). We conclude that the LA study that we challenged was robust against fake learners. While these results carry an optimistic
message on the trustworthiness of LA research, they rely on data from one MOOC. We believe that this issue should receive more attention within the LA research community, and can explain some ‘surprising’ research results in MOOCs. Keywords: Learning Analytics, Educational Data Mining, MOOCs, Fake Learners, Reliability, IR
Analyzing the behavior of students regarding learning activities, badges, and academic dishonesty in MOOC environment
Mención Internacional en el tÃtulo 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 traÃdo nuevas oportunidades de mejorar la mayorÃa
de productos y servicios, incluyendo la educación. El aprendizaje mediante tecnologÃas web se
ha extendido mucho durante la última década, que conjuntamente con el fenómeno de los cursos
abiertos masivos en lÃnea (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, especÃficamente en el contexto de MOOCs y cursos privados pequeños
en lÃnea (SPOCs). En la tesis nos hemos enfocado en el análisis de datos en las plataformas Khan
Academy, Open edX y Coursera. Más especÃficamente, vamos a trabajar en interfaces de visualizaciones
de analÃtica de aprendizaje, llevando a cabo la evaluación de estas herramientas
de analÃtica 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 estadÃstica, como correlaciones
y comparaciones entre grupos, asà 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, asà como la
evaluación de interfaces de analÃtica visual, proporcionando recomendaciones que pueden ayudar
a mejorar futuras experiencias educacionales.Programa Oficial de Doctorado en IngenierÃa TelemáticaPresidente: Davinia Hernández Leo.- Secretario: Luis Sánchez Fernández.- Vocal: Adolfo Ruiz Callej
Comprendiendo el potencial y los desafÃos del Big Data en las escuelas y la educación
In recent years, the world has experienced a huge revolution centered around the gathering and application of big data in various fields. This has affected many aspects of our daily life, including government, manufacturing, commerce, health, communication, entertainment, and many more. So far, education has benefited only a little from the big data revolution. In this article, we review the potential of big data in the context of education systems. Such data may include log files drawn from online learning environments, messages on online discussion forums, answers to open-ended questions, grades on various tasks, demographic and administrative information, speech, handwritten notes, illustrations, gestures and movements, neurophysiologic signals, eye movements, and many more. Analyzing this data, it is possible to calculate a wide range of measurements of the learning process and to support various educational stakeholders with informed decision-making. We offer a framework for better understanding of how big data can be used in education. The framework comprises several elements that need to be addressed in this context: defining the data; formulating data-collecting and storage apparatuses; data analysis and the application of analysis products. We further review some key opportunities and some important challenges of using big data in educationEn los últimos años, el mundo ha experimentado una gran revolución centrada en la recopilación y aplicación de big data en varios campos. Esto ha afectado muchos aspectos de nuestra vida diaria, incluidos el gobierno, la manufactura, el comercio, la salud, la comunicación, el entretenimiento y muchos más. Hasta ahora, la educación se ha beneficiado muy poco de la revolución del big data. En este artÃculo revisamos el potencial de los macrodatos en el contexto de los sistemas educativos. Dichos datos pueden incluir archivos de registro extraÃdos de entornos de aprendizaje en lÃnea, mensajes en foros de discusión en lÃnea, respuestas a preguntas abiertas, calificaciones en diversas tareas, información demográfica y administrativa, discurso, notas escritas a mano, ilustraciones, gestos y movimientos, señales neurofisiológicas, movimientos oculares y muchos más. Analizando estos datos es posible calcular una amplia gama de mediciones del proceso de aprendizaje y apoyar a diversos interesados educativos con una toma de decisiones informada. Ofrecemos un marco para una mejor comprensión de cómo se puede utilizar el big data en la educación. El marco comprende varios elementos que deben abordarse en este contexto: definición de los datos; formulación de aparatos de recolección y almacenamiento de datos; análisis de datos y aplicación de productos de análisis. Además, revisamos algunas oportunidades clave y algunos desafÃos importantes del uso de big data en la educació
EOSC Synergy WP6: Initial review of systems, initiatives and development of selection criteria of the online learning/training platforms and initiatives
This report describes a review of possible learning platforms and tools, and relevant previous and current projects and initiatives in the area of Open Science and EOSC training and education. It also includes reflections on the criteria we will use to select the platform and tools for the EOSC-Synergy project.European Commission. The report is a deliverable of EOSC-synergy project (INFRAEOSC-05(b)), Grant agreement ID: 857647.Peer reviewe
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