563 resultados para Online Learning
Resumo:
The unprecedented attacks of September 11, 2001, and the subsequent anthrax-related events thrust our nation's often forgotten public health system into the forefront of public attention. A strong public health system with a well-prepared workforce plays a critical role in preparing for and responding to the threat of bioterrorism and other disasters and emergencies. Technical expertise is critical as is a basic awareness and understanding of core public health competencies especially as they relate to disaster and emergency response is also imperative for a public health agency to function as a vital Emergency Response team member. Ideally this training should begin at the Public Health graduate level so as to provide the baseline core tools to be able to function as a vital team member when they are practicing out in the real world. Online learning is an efficient and effective method for providing public health education to in a flexible format to meet the needs of busy student-professions. This Public Health Disaster Preparedness online course developed during an Emergency Response state program practicum is a practical and proficient approach to accomplish this endeavor. ^
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Objectives. The objectives of this report were to describe current best standards in online education, class competencies, class objectives, class activities and to compare the class competencies, objectives and activities undertaken with the current best practices in online teaching and to provide a list of recommendations based on the most efficacious practices. ^ Methods. Utilizing the key words- online teaching, national standards, quality, online courses, I: (1) conducted a search on Google to find the best standard for quality online courses; the search yielded National Standards for Quality Online Teaching as the gold standard in online course quality; (2) specified class objectives and competencies as well as major activities undertaken as a part of the class. Utilizing the Southern Regional Education Board evaluation checklist for online courses, I: (1) performed an analysis comparing the class activities, objectives, and competencies with the current best standards; (2) utilized the information obtained from the analysis and class experiences to develop recommendations for the most efficacious online teaching practices. ^ Results. The class met the criteria set by the Southern Regional Education Board for evaluating online classes completely in 75%, partially in 16% and did not meet the criteria in 9% cases. The majority of the parameters in which the class did not meet the standards (4 of 5) were due to technological reasons beyond the scope of the class instructor, teaching assistant and instructional design. ^ Discussion. Successful online teaching requires awareness of technology, good communication, methods, collaboration, reflection and flexibility. Creation of an online community, engaging online learners and utilizing different learning styles and assessment methods promote learning. My report proposes that online teaching should actively engage the students and teachers with multiple interactive strategies as evidenced from current best standards of online education and my “hands-on” work experience. ^ Conclusion. The report and the ideas presented are intended to create a foundation for efficacious practice on the online teaching platform. By following many of the efficacious online practices described in the report and adding from their own experiences, online instructors and teaching assistants can contribute to effective online learning. ^
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Nowadays, online learning is booming. Really "booming", actually: thousands of online courses, hundreds of researching groups, dozens of universities online. Eventually, Web Based Learning has left the labs, and begun a fruitful life in the "real world". However,quantity has little to do with "real innovation". In very rare occasions, online courses and teaching institutions are breaking with the rules of the Gutenberg Galaxy: the rules developed during five centuries of printing books. They are designed on a linear basis,and based on conventional text.
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In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. In addition to recording TOD, the cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also identified for use as the independent variables in the regression analysis. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajectory parame- ters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowledge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace.
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Aircraft tracking plays a key and important role in the Sense-and-Avoid system of Unmanned Aerial Vehicles (UAVs). This paper presents a novel robust visual tracking algorithm for UAVs in the midair to track an arbitrary aircraft at real-time frame rates, together with a unique evaluation system. This visual algorithm mainly consists of adaptive discriminative visual tracking method, Multiple-Instance (MI) learning approach, Multiple-Classifier (MC) voting mechanism and Multiple-Resolution (MR) representation strategy, that is called Adaptive M3 tracker, i.e. AM3. In this tracker, the importance of test sample has been integrated to improve the tracking stability, accuracy and real-time performances. The experimental results show that this algorithm is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant surrounding illumination, partial aircraft occlusion, blur motion, rapid pose variation and onboard mechanical vibration, low computation capacity and delayed information communication between UAVs and Ground Station (GS). To our best knowledge, this is the first work to present this tracker for solving online learning and tracking freewill aircraft/intruder in the UAVs.
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This paper presents a novel robust visual tracking framework, based on discriminative method, for Unmanned Aerial Vehicles (UAVs) to track an arbitrary 2D/3D target at real-time frame rates, that is called the Adaptive Multi-Classifier Multi-Resolution (AMCMR) framework. In this framework, adaptive Multiple Classifiers (MC) are updated in the (k-1)th frame-based Multiple Resolutions (MR) structure with compressed positive and negative samples, and then applied them in the kth frame-based Multiple Resolutions (MR) structure to detect the current target. The sample importance has been integrated into this framework to improve the tracking stability and accuracy. The performance of this framework was evaluated with the Ground Truth (GT) in different types of public image databases and real flight-based aerial image datasets firstly, then the framework has been applied in the UAV to inspect the Offshore Floating Platform (OFP). The evaluation and application results show that this framework is more robust, efficient and accurate against the existing state-of-art trackers, overcoming the problems generated by the challenging situations such as obvious appearance change, variant illumination, partial/full target occlusion, blur motion, rapid pose variation and onboard mechanical vibration, among others. To our best knowledge, this is the first work to present this framework for solving the online learning and tracking freewill 2D/3D target problems, and applied it in the UAVs.
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In the past decades, online learning has transformed the educational landscape with the emergence of new ways to learn. This fact, together with recent changes in educational policy in Europe aiming to facilitate the incorporation of graduate students to the labor market, has provoked a shift on the delivery of instruction and on the role played by teachers and students, stressing the need for development of both basic and cross-curricular competencies. In parallel, the last years have witnessed the emergence of new educational disciplines that can take advantage of the information retrieved by technology-based online education in order to improve instruction, such as learning analytics. This study explores the applicability of learning analytics for prediction of development of two cross-curricular competencies – teamwork and commitment – based on the analysis of Moodle interaction data logs in a Master’s Degree program at Universidad a Distancia de Madrid (UDIMA) where the students were education professionals. The results from the study question the suitability of a general interaction-based approach and show no relation between online activity indicators and teamwork and commitment acquisition. The discussion of results includes multiple recommendations for further research on this topic.
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Trabalho de projeto de mestrado, Educação (Área de especialidade em Educação e Tecnologias Digitais), Universidade de Lisboa, Instituto de Educação, 2015
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The potential of online learning has long afforded the hope of providing quality education to anyone, anywhere in the world. The recent development of Massive Open Online Courses (MOOCs) heralded an exciting new breakthrough by providing free academic instruction and professional skills development from the world’s leading universities to anyone with the sufficient resources to access the internet. The research in Advancing MOOCs for Development Initiative study was designed to analyze the MOOC landscape in developing countries and to better understand the motivations of MOOC users and afford insights on the advantages and limitations of MOOCs for workforce development outcomes. The key findings of this study challenge commonly held beliefs about MOOC usage in developing countries, defying typical characterizations of how people in resource constrained settings use technology for learning and employment. In fact, some of the findings are so contrary to what has been reported in the U.S. and other developed environments that they raise new questions for further investigation.
Resumo:
The potential of online learning has long afforded the hope of providing quality education to anyone, anywhere in the world. The recent development of Massive Open Online Courses (MOOCs) heralded an exciting new breakthrough by providing free academic instruction and professional skills development from the world’s leading universities to anyone with the sufficient resources to access the internet. The research in Advancing MOOCs for Development Initiative study was designed to analyze the MOOC landscape in developing countries and to better understand the motivations of MOOC users and afford insights on the advantages and limitations of MOOCs for workforce development outcomes. The key findings of this study challenge commonly held beliefs about MOOC usage in developing countries, defying typical characterizations of how people in resource constrained settings use technology for learning and employment. In fact, some of the findings are so contrary to what has been reported in the U.S. and other developed environments that they raise new questions for further investigation.
Resumo:
The potential of online learning has long afforded the hope of providing quality education to anyone, anywhere in the world. The recent development of Massive Open Online Courses (MOOCs) heralded an exciting new breakthrough by providing free academic instruction and professional skills development from the world’s leading universities to anyone with the sufficient resources to access the internet. The research in Advancing MOOCs for Development Initiative study was designed to analyze the MOOC landscape in developing countries and to better understand the motivations of MOOC users and afford insights on the advantages and limitations of MOOCs for workforce development outcomes. The key findings of this study challenge commonly held beliefs about MOOC usage in developing countries, defying typical characterizations of how people in resource constrained settings use technology for learning and employment. In fact, some of the findings are so contrary to what has been reported in the U.S. and other developed environments that they raise new questions for further investigation.
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Virtual territories and their theme parks are more akin to the physical world of real estate than they might at first appear. The trick in triggering the designer's imagination, is to find a 'nice renovator' (cottage/ house) at a low price, with loads of potential, and by doing it on the cheap to add character, and engage the imagination. Here the designer can construct changes from an imagined space. Vision is more important than how the actual place presents.This work describes a case study involving undergraduate students in the Creative Industries who needed a place to explore, so as to create their own visions and projects. The place had to inspire, trigger engagement, and their imaginations. At the same time it was important that the place did not coerce activity, or distract from the task by confusing tools with task, or architectural navigation with conceptual skills.The solution was an alternate reality.
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We propose a Bayesian framework for regression problems, which covers areas which are usually dealt with by function approximation. An online learning algorithm is derived which solves regression problems with a Kalman filter. Its solution always improves with increasing model complexity, without the risk of over-fitting. In the infinite dimension limit it approaches the true Bayesian posterior. The issues of prior selection and over-fitting are also discussed, showing that some of the commonly held beliefs are misleading. The practical implementation is summarised. Simulations using 13 popular publicly available data sets are used to demonstrate the method and highlight important issues concerning the choice of priors.
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In recent years there has been an increased interest in applying non-parametric methods to real-world problems. Significant research has been devoted to Gaussian processes (GPs) due to their increased flexibility when compared with parametric models. These methods use Bayesian learning, which generally leads to analytically intractable posteriors. This thesis proposes a two-step solution to construct a probabilistic approximation to the posterior. In the first step we adapt the Bayesian online learning to GPs: the final approximation to the posterior is the result of propagating the first and second moments of intermediate posteriors obtained by combining a new example with the previous approximation. The propagation of em functional forms is solved by showing the existence of a parametrisation to posterior moments that uses combinations of the kernel function at the training points, transforming the Bayesian online learning of functions into a parametric formulation. The drawback is the prohibitive quadratic scaling of the number of parameters with the size of the data, making the method inapplicable to large datasets. The second step solves the problem of the exploding parameter size and makes GPs applicable to arbitrarily large datasets. The approximation is based on a measure of distance between two GPs, the KL-divergence between GPs. This second approximation is with a constrained GP in which only a small subset of the whole training dataset is used to represent the GP. This subset is called the em Basis Vector, or BV set and the resulting GP is a sparse approximation to the true posterior. As this sparsity is based on the KL-minimisation, it is probabilistic and independent of the way the posterior approximation from the first step is obtained. We combine the sparse approximation with an extension to the Bayesian online algorithm that allows multiple iterations for each input and thus approximating a batch solution. The resulting sparse learning algorithm is a generic one: for different problems we only change the likelihood. The algorithm is applied to a variety of problems and we examine its performance both on more classical regression and classification tasks and to the data-assimilation and a simple density estimation problems.
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Operations Management, 3rd Edition provides a clear and accessible introduction to this important area of study, focusing on all key areas of operations in both manufacturing and service industries. Features: Focuses on the subject from a European perspective. Deals with the management of the creation of goods and the delivery of services to the customer. Covers the main areas of operations strategy, the design of operations system and the management of operations over time. Incorporates more strategic and international commentary. Includes a strategy link section consisting of a paragraph relating each chapter topic to operations strategy. Includes more end of chapter and quantitative exercises. Cases have been updated throughout and now include: Service including public sector, international, a mix of mini–cases and a longer case for each chapter. Accompanied by a comprehensive package of online learning support materials including: A robust testbank featuring 1500 questions, PowerPoint slides and a comprehensive instructor's manual An interactive e–Book is included with every new copy of this text, featuring a wealth of embedded media, including: Animated worked examples, simulations, virtual tours, videos, flashcards and practice quizzes.