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

On small black holes, KK monopoles and solitonic 5-branes

We review and extend results on higher-curvature corrections to different configurations describing a superposition of heterotic strings, KK monopoles, solitonic 5-branes and momentum waves. Depending on which sources are present, the low-energy fields describe a black hole, a soliton or a naked singularity. We show that this property is unaltered when perturbative higher-curvature corrections are included, provided the sources are fixed. On the other hand, this character may be changed by appropriate introduction (or removal) of sources regardless of the presence of curvature corrections, which constitutes a non-perturbative modification of the departing system. The general system of multicenter KK monopoles and their 5-brane charge induced by higher-curvature corrections is discussed in some detail, with special attention paid to the possibility of merging monopoles. Our results are particularly relevant for small black holes (Dabholkar-Harvey states, DH), which remain singular after quadratic curvature corrections are taken into account. When there are four non-compact dimensions, we notice the existence of a black hole with regular horizon whose entropy coincides with that of the DH states, but the charges and supersymmetry preserved by both configurations are different. A similar construction with five non-compact dimensions is possible, in this case with the same charges as DH, although it fails to reproduce the DH entropy and supersymmetry. No such configuration exists if $d>5$, which we interpret as reflecting the necessity of having a 5-brane wrapping the compact space

Analítica del aprendizaje y educación basada en datos: Un campo en expansión

The growing presence of digital mediation systems in most educational spaces —whether face-to-face or not, formalized or open, and at basic or lifelong learning levels— has accelerated the advance of learning analytics and the use of data in education as a common practice. Using digital educational tools facilitates the interaction between students, teachers and learning resources in the digital world, and generates a remarkable volume of data that can be analyzed by applying a variety of methodologies. Thus, research focused on information generated by student activity in digital spaces has risen exponentially. Based on this evidence, this special issue shows a set of studies in the field of data-driven educational research and the field of digital learning, which enriches knowledge about learning processes and management of teaching in digitally mediated spaces.La creciente utilización de sistemas de mediación digital en la mayoría de espacios educativos —ya sean presenciales o no, formales o abiertos, y tanto en el nivel de educación básica como en situaciones de aprendizaje a lo largo de la vida— está acelerando el avance de la analítica del aprendizaje y haciendo que el uso de la información digital sea una práctica común en el campo de la educación. Las herramientas educativas digitales facilitan la interacción entre estudiantes, profesores y recursos de aprendizaje, y generan de manera continua un notable volumen de datos que pueden analizarse aplicando una variedad de metodologías. Esto ha hecho que aumenten exponencialmente las investigaciones que toman como referencia la información que procede de la actividad de los estudiantes en esos espacios digitales. Partiendo de esas evidencias, este número especial muestra un conjunto de estudios en el campo del aprendizaje digital y la investigación educativa basada en datos, que enriquecen el conocimiento sobre los procesos de aprendizaje y la gestión de la enseñanza en espacios mediados digitalmente

Black Hole Multipoles in Higher-Derivative Gravity

We consider a broad family of higher-derivative extensions of four-dimensional Einstein gravity and study the multipole moments of rotating black holes therein. We carefully show that the various definitions of multipoles carry over from general relativity, and compute these multipoles for higher-derivative Kerr using the ACMC expansion formalism. We obtain the mass $M_{n}$ and current $S_{n}$ multipoles as a series expansions in the dimensionless spin; in some cases we are able to resum these series into closed-form expressions. Moreover, we observe the existence of intriguing relations between the corrections to the parity-odd multipoles $S_{2n}\neq 0$ and $M_{2n+1}\neq 0$ that break equatorial symmetry, and the parity-preserving corrections that only modify $S_{2n+1}$ and $M_{2n}$. Further, we comment on the higher-derivative corrections to multipole ratios for Kerr, and we discuss the phenomenological implications of the corrections to the multipole moments for current and future gravitational wave experiments.Comment: 31 pages + Appendix, 13 figures, Mathematica notebook with all expansions used in the pape

Identifying Experts in Question \& Answer Portals: A Case Study on Data Science Competencies in Reddit

The irreplaceable key to the triumph of Question & Answer (Q&A) platforms is their users providing high-quality answers to the challenging questions posted across various topics of interest. Recently, the expert finding problem attracted much attention in information retrieval research. In this work, we inspect the feasibility of supervised learning model to identify data science experts in Reddit. Our method is based on the manual coding results where two data science experts labelled expert, non-expert and out-of-scope comments. We present a semi-supervised approach using the activity behaviour of every user, including Natural Language Processing (NLP), crowdsourced and user feature sets. We conclude that the NLP and user feature sets contribute the most to the better identification of these three classes It means that this method can generalise well within the domain. Moreover, we present different types of users, which can be helpful to detect various types of users in the future

A Survey on Data-Driven Evaluation of Competencies and Capabilities Across Multimedia Environments

The rapid evolution of technology directly impacts the skills and jobs needed in the next decade. Users can, intentionally or unintentionally, develop different skills by creating, interacting with, and consuming the content from online environments and portals where informal learning can emerge. These environments generate large amounts of data; therefore, big data can have a significant impact on education. Moreover, the educational landscape has been shifting from a focus on contents to a focus on competencies and capabilities that will prepare our society for an unknown future during the 21st century. Therefore, the main goal of this literature survey is to examine diverse technology-mediated environments that can generate rich data sets through the users’ interaction and where data can be used to explicitly or implicitly perform a data-driven evaluation of different competencies and capabilities. We thoroughly and comprehensively surveyed the state of the art to identify and analyse digital environments, the data they are producing and the capabilities they can measure and/or develop. Our survey revealed four key multimedia environments that include sites for content sharing & consumption, video games, online learning and social networks that fulfilled our goal. Moreover, different methods were used to measure a large array of diverse capabilities such as expertise, language proficiency and soft skills. Our results prove the potential of the data from diverse digital environments to support the development of lifelong and lifewide 21st-century capabilities for the future society

Analyzing and testing viewability methods in an advertising network

Many of the current online businesses base completely their revenue models in earnings from online advertisement. A problematic fact is that according to recent studies more than half of display ads are not being detected as viewable. The International Advertising Bureau (IAB) has defined a viewable impression as an impression that at least 50% of its pixels are rendered in the viewport during at least one continuous second. Although there is agreement on this definition for measuring viewable impressions in the industry, there is no systematic methodologies on how it should be implemented or the trustworthiness of these methods. In fact, the Media Rating Council (MRC) announced that there are inconsistencies across multiple reports attempting to measure this metric. In order to understand the magnitude of the problem, we conduct an analysis of different methods to track viewable impressions. Then, we test a subset of geometric and strong interaction methods in a webpage registered in the worldwide ad-network ExoClick, which currently serves over 7 billion geo-targeted ads a day to a global network of 65000 web/mobile publisher platforms. We find that the Intersection Observer API is the method that detects more viewable impressions given its robustness towards the technological constraints that face the rest of implementations available. The motivation of this work is to better understand the limitations and advantages of such methods, which can have an impact at a standardisation level in online advertising industry, as well as to provide guidelines for future research based on the lessons learned.This work was possible thanks to the support of “Plan de Doctorados Industriales de la Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Catalunya” and the Spanish Ministry of Economy and Competitiveness through the Juan de la Cierva Formación program (FJCI-2017-34926). We also want to thank ExoClick for their support in conducting thisresearchPeer ReviewedPostprint (published version