What learning analytics based prediction models tell us about feedback preferences of students

Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners

Using moodle analytics for continuous e-assessment in a financial mathematics course at Polytechnic of Porto

The relevance of electronic learning, commonly called e-learning, has been growing exponentially in the last decade. Virtual learning environments (VLEs) disclosed new paths for interactions and motivation promotion, offering basic learning analytics functions and are becoming progressively popular. Moodle (acronym for Modular Object Oriented Dynamic Learning Environment) is one of the most used VLEs, it is a free learning management system distributed as Open Source. The VLE Moodle gives professors access to an “endless” use and performance database like the number of downloads for each resource, participation of students in courses, statistics of performed quizzes, among others. The data stored by Moodle offers a good and handy source for learning analytics. One popular definition, from the First International Conference on Learning Analytics and Knowledge in 2011, states that “Learning Analytics is the measurement, collection, analysis and reporting of data about students and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. Thus, using appropriate learning analytics methods and techniques, it would be helpful to analyze what particular learning activities or tools were practically used by students in Moodle, and to what extent. Considering the importance of the student engagement and the benefits of continuous assessment in higher education, as well as the impact of information and communications technology (ICT) on educational processes, it is important to integrate technology into continuous assessment practices. Since student engagement is connected to the quality of the student experience, increasing it is one way of enhancing quality in a higher education institution. In this study, will be demonstrated how the use of several educational resources and a low-stakes continuous weekly e-assessment in Moodle had a positive influence on student engagement in a second year undergraduate Financial Mathematics Course. Students felt that their increased engagement and improved learning was a straight result of this method. Furthermore, this suggests that wisely planned assignments and assessments can be used to increase student engagement and learning, and, as a result, contribute to improving the quality of student experience and success.info:eu-repo/semantics/publishedVersio

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

Comprendiendo el potencial y los desafíos 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 artículo revisamos el potencial de los macrodatos en el contexto de los sistemas educativos. Dichos datos pueden incluir archivos de registro extraídos de entornos de aprendizaje en línea, mensajes en foros de discusión en línea, 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 desafíos importantes del uso de big data en la educació

A Mobile App Platform for Discovering Learning Profiles and Analytics

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Data Visualization in Online Educational Research

This chapter presents a general and practical guideline that is intended to introduce the traditional visualization methods (word clouds), and the advanced visualization methods including interactive visualization (heatmap matrix) and dynamic visualization (dashboard), which can be applied in quantitative, qualitative, and mixed-methods research. This chapter also presents the potentials of each visualization method for assisting researchers in choosing the most appropriate one in the web-based research study. Graduate students, educational researchers, and practitioners can contribute to take strengths from each visual analytical method to enhance the reach of significant research findings into the public sphere. By leveraging the novel visualization techniques used in the web-based research study, while staying true to the analytical methods of research design, graduate students, educational researchers, and practitioners will gain a broader understanding of big data and analytics for data use and representation in the field of education