FORGE: An eLearning Framework for Remote Laboratory Experimentation on FIRE Testbed Infrastructure

The Forging Online Education through FIRE (FORGE) initiative provides educators and learners in higher education with access to world-class FIRE testbed infrastructure. FORGE supports experimentally driven research in an eLearning environment by complementing traditional classroom and online courses with interactive remote laboratory experiments. The project has achieved its objectives by defining and implementing a framework called FORGEBox. This framework offers the methodology, environment, tools and resources to support the creation of HTML-based online educational material capable accessing virtualized and physical FIRE testbed infrastruc- ture easily. FORGEBox also captures valuable quantitative and qualitative learning analytic information using questionnaires and Learning Analytics that can help optimise and support student learning. To date, FORGE has produced courses covering a wide range of networking and communication domains. These are freely available from FORGEBox.eu and have resulted in over 24,000 experiments undertaken by more than 1,800 students across 10 countries worldwide. This work has shown that the use of remote high- performance testbed facilities for hands-on remote experimentation can have a valuable impact on the learning experience for both educators and learners. Additionally, certain challenges in developing FIRE-based courseware have been identified, which has led to a set of recommendations in order to support the use of FIRE facilities for teaching and learning purposes

A literature synthesis of personalised technology-enhanced learning: what works and why

Personalised learning, having seen both surges and declines in popularity over the past few decades, is once again enjoying a resurgence. Examples include digital resources tailored to a particular learner’s needs, or individual feedback on a student’s assessed work. In addition, personalised technology-enhanced learning (TEL) now seems to be attracting interest from philanthropists and venture capitalists indicating a new level of enthusiasm for the area and a potential growth industry. However, these industries may be driven by profit rather than pedagogy, and hence it is vital these new developments are informed by relevant, evidence-based research. For many people, personalised learning is an ambiguous and even loaded term that promises much but does not always deliver. This paper provides an in-depth and critical review and synthesis of how personalisation has been represented in the literature since 2000, with a particular focus on TEL. We examine the reasons why personalised learning can be beneficial and examine how TEL can contribute to this. We also unpack how personalisation can contribute to more effective learning. Lastly, we examine the limitations of personalised learning and discuss the potential impacts on wider stakeholders

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

¿Pueden los MOOC cerrar la brecha de oportunidades?: La contribución del diseño pedagógico social inclusivo

Massive Open Online Courses (MOOCs) are open courses made available online at no cost to the user and designed to scale up, allowing for a large number of participants. As such, they are a disruptive new development which has the potential to widen access to higher education since they contribute to social inclusion, the dissemination of knowledge and pedagogical innovation. However, assuring quality learning opportunities to all cannot be simply reduced to allowing free access to higher education. On the contrary, it implies assuring equitable opportunities for every participant to succeed in their learning experience. This goal depends on the quality of the learning design. To be successful, a massive open online learning experience has to empower learners and to facilitate a networked learning environment. In fact, MOOCs are designed to serve a high heterogeneity of profiles, with many differences regarding learning needs and preferences, prior knowledge, contexts of participation and diversity of online platforms. Personalization can play a key role in this process. In this article, the authors describe the iMOOC pedagogical model and its later derivative, the sMOOC model, and explain how they contributed to the introduction of the principles of diversity and learner equity to MOOC design, allowing for a clear differentiation of learning paths and also of virtual environments, while empowering participants to succeed in their learning experiences. Using a design-based research approach, a comparative analysis of two course iterations each representing each model is also presented and discussed.Los cursos en línea abiertos y masivos (MOOC) son cursos abiertos disponibles en línea sin costo para el usuario y diseñados para ampliarse, permitiendo un gran número de participantes. Como tales, son un nuevo desarrollo disruptivo que tiene el potencial de ampliar el acceso a la educación superior, ya que contribuyen a la inclusión social, la difusión del conocimiento y la innovación pedagógica. Sin embargo, garantizar oportunidades de aprendizaje de calidad para todos no puede reducirse simplemente a permitir el acceso gratuito a la educación superior. Por el contrario, implica asegurar oportunidades equitativas para que cada participante tenga éxito en su experiencia de aprendizaje. Este objetivo depende de la calidad del diseño de aprendizaje. Para tener éxito, una experiencia de aprendizaje en línea abierta y masiva debe empoderar a los alumnos y facilitar un entorno de aprendizaje en red. De hecho, los MOOC están diseñados para servir a una gran heterogeneidad de perfiles, con muchas diferencias con respecto a las necesidades y preferencias de aprendizaje, conocimiento previo, contextos de participación y diversidad de plataformas en línea. La personalización puede jugar un papel clave en este proceso. En este artículo, los autores describen el modelo pedagógico iMOOC y su derivada posterior, el modelo sMOOC, y explican cómo contribuyeron a la introducción de los principios de diversidad y equidad en el diseño MOOC, lo que permite una clara diferenciación de las rutas de aprendizaje y también de entornos virtuales, al tiempo que permite a los participantes tener éxito en sus experiencias de aprendizaje. Usando un enfoque de design-based research, también se presenta y discute un análisis comparativo de dos iteraciones del curso, cada una representando cada modelo

Exploring scholarly data with Rexplore.

Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves

Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments. We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments