Página 17 dos resultados de 88805 itens digitais encontrados em 0.056 segundos

## Expectativas do e-learning na política de língua do ensino do português no estrangeiro

Silva, Isabel Cristina Rodrigues Heleno
Relevância na Pesquisa
36.28%
Dissertação de Mestrado em Português Língua não Materna apresentada à Universidade Aberta; O presente trabalho de investigação, enquadra-se no decurso da formação, ao nível do Mestrado em Português Língua Não Materna, tendo como principal objetivo na compreensão e reflexão dos modelos pedagógicos em contexto de educação via E-learning, centrado nas expetativas dos aprendentes, face a novas políticas de língua do ensino do português no estrangeiro e sua problemática perante um conjunto de fatores associados. Deste modo, para desenvolvimento deste estudo, contou-se com a colaboração de cento e cinquenta e seis aprendentes e optamos por utilizar como metodologia de recolha de dados na aplicação de questionário on-line, feita aos aprendentes do português língua estrangeira, no ensino primário e secundário da rede escolar na Europa. Posto isto, salientamos que os aprendentes apesar de terem uma enorme motivação no uso das novas tecnologias com recurso à Internet, na aprendizagem do português língua não materna, ainda assim não desejam abdicar da presença indispensável do professor dentro de uma sala de aula. A resistência à modalidade E-learning é notável, quando apontam falhas na aquisição e desenvolvimento das competências linguísticas...

## "Novas abordagens em aprendizado de máquina para a geração de regras, classes desbalanceadas e ordenação de casos" ; "New approaches in machine learning for rule generation, class imbalance and rankings"

Prati, Ronaldo Cristiano
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Relevância na Pesquisa
36.28%

## Faces e falas da avaliação universitária : o portfólio como recurso mediador da aprendizagem; Faces and speeches of the assessment in the higher education learning : the portfolio as a learning mediator resource

Maria Lourdes Vieira
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Relevância na Pesquisa
36.28%

## Aprendizagem situada em uma comunidade de aprendizes de matemática de uma escola pública; Situated learning in a community of learners of mathematics in a public school

Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Relevância na Pesquisa
36.28%
A pesquisa em questão tem como objetivo identificar, problematizar e compreender a aprendizagem que ocorre nas aulas de matemática de um 6º ano de uma escola pública. Para tanto, foi tomado como objeto de estudo a prática pedagógica de uma professora de matemática, pesquisadora e autora desta tese, e as interações com seus alunos de uma turma de 6º ano, ao longo de um ano letivo (2009). A sua questão central é Que aprendizagens são produzidas no interior de uma comunidade de aprendizes de matemática e como esses participantes se transformam identitariamente, apropriando-se de saberes/práticas nessa/dessa comunidade? Trata-se de uma pesquisa qualitativa, com algumas características da etnografia. O processo de coletas de dados e de documentação teve por objetivo fornecer subsídios para as interpretações e análises acerca das práticas e indícios de aprendizagem dos alunos e da própria comunidade de aprendizes. Os procedimentos metodológicos foram: questionário do início do ano letivo; videogravação de algumas aulas; pequenos relatos sobre as aulas; carta relato sobre uma aula de grandezas e medidas enviada a um destinatário a ser escolhido pelo aluno; produções dos alunos e diário de campo da pesquisadora. Os principais aportes teóricos são aqueles relativos à teoria social da aprendizagem em comunidade de prática...

## Estudo para a implementação de plataformas de e-learning no sistema de formação dos recursos humanos da saúde: o caso particular dos enfermeiros de um hospital privado; Study for the implementation of e-learning platforms in the training of health human resources: the special case of a private hospital nurses

Lopes, Cristina Maria C. Passos
Relevância na Pesquisa
36.28%

## Vantagens e desvantagens no desenvolvimento de recursos educativos para dispositivos móveis. CV Learning Mobile - Software educativo sobre “Organização Administrativa de Cabo Verde”

Zego, José Afonso Mendes Tavares
Fonte: Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto Publicador: Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto
Relevância na Pesquisa
36.28%
Não é recente a contribuição das tecnologias de informação e comunicação em processos de ensino/aprendizagem, no sentido da proliferação de conhecimento, de forma fácil e rápida. Com a contínua evolução tecnológica, surgem novos conceitos relativamente a processos de ensino/aprendizagem assentes nessas tecnologias. A aprendizagem por meio de dispositivos móveis, o m-Learning, é um exemplo, sendo um campo de investigação educacional em franca evolução, que explora essencialmente a mobilidade e a interactividade. No âmbito desta dissertação, pretende-se analisar a tecnologia m-Learning, fazendo referência as principais vantagens e desvantagens desta tecnologia. Neste sentido, e por pretendermos dar o nosso contributo ao ensino cabo-verdiano, onde a utilização de tal tecnologia é ainda inexistente, desenvolveu-se a aplicação CV Learning Mobile, um software educativo sobre a “Organização Administrativa de Cabo Verde”, como resultado do estudo efectuado.; It is not recent the contribution of information and communication technologies in teaching / learning processes, in the sense of the knowledge proliferation, in an easy and fast way. With the continuous technological evolution, new concepts emerge linked to processes based on these technologies. The learning through mobile devices...

## Actitudes de los directivos en torno a la adopción del e-learning como herramienta de trabajo en las empresas

Rodríguez Bello, Olga Susana; Achury Penha, Diana Marcela
Tipo: info:eu-repo/semantics/masterThesis; info:eu-repo/semantics/acceptedVersion Formato: application/pdf
Relevância na Pesquisa
36.28%

## Communication, Affect, & Learning in the Classroom

Wrench, Jason S.; Peck Richmond, Virginia; Gorham, Joan
Fonte: Metabiblioteca Livros Publicador: Metabiblioteca Livros
Tipo: Livro Formato: application/pdf
Português
Relevância na Pesquisa
36.28%
The purpose of the handbook was to synthesize the first three decades of research in instructional communication into a single volume that could help both researchers and instructors understand the value of communication in the instructional process.; Preface; 1.Teaching As a Communication Process The Instructional Communication Process The Teacher The Content The Instructional Strategy The Student The Feedback/Evaluation The Learning Environment/Instructional Context Kibler’s Model of Instruction The ADDIE Model of Instructional Design; 2.Communicating With Instructional Objectives Why Some Teachers Resent Objectives The Value of Objectives What Objectives Should Communicate; 3.Instructional Communication Strategies The Teacher As a Speaker The Teacher As a Moderator The Teacher As a Trainer The Teacher As a Manager The Teacher As a Coordinator & Innovator; 4.Communication, Affect, and Student Needs Measuring Student Affect Basic Academic Needs of Students Traditional Interpersonal Need Models Outcomes of Meeting Student Needs; 5.Learning Styles What is Learning Style? Dimensions of Learning Style and Their Assessment Matching, Bridging, and Style-Flexing; 6.Classroom Anxieties and Fears Communication Apprehension Receiver Apprehension Writing Apprehension Fear of Teacher Evaluation Apprehension Classroom Anxiety Probable Causes of Classroom Anxiety Communication Strategies for Reducing Classroom Anxiety; 7.Communication And Student Self-Concept Student Self-Concept: Some Definitions Characteristics of the Self Development of Student Self-Concept Dimensions of Student Self-Concept Self-Concept and Academic Achievement Effects of Self-Concept on Achievement Poker Chip Theory of Learning Communication Strategies for Nurturing and Building Realistic Student Self-Concept; 8.Instructional Assessment:Feedback...

## Differentially Private Online Learning

Jain, Prateek; Kothari, Pravesh; Thakurta, Abhradeep
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.28%
In this paper, we consider the problem of preserving privacy in the online learning setting. We study the problem in the online convex programming (OCP) framework---a popular online learning setting with several interesting theoretical and practical implications---while using differential privacy as the formal privacy measure. For this problem, we distill two critical attributes that a private OCP algorithm should have in order to provide reasonable privacy as well as utility guarantees: 1) linearly decreasing sensitivity, i.e., as new data points arrive their effect on the learning model decreases, 2) sub-linear regret bound---regret bound is a popular goodness/utility measure of an online learning algorithm. Given an OCP algorithm that satisfies these two conditions, we provide a general framework to convert the given algorithm into a privacy preserving OCP algorithm with good (sub-linear) regret. We then illustrate our approach by converting two popular online learning algorithms into their differentially private variants while guaranteeing sub-linear regret ($O(\sqrt{T})$). Next, we consider the special case of online linear regression problems, a practically important class of online learning problems, for which we generalize an approach by Dwork et al. to provide a differentially private algorithm with just $O(\log^{1.5} T)$ regret. Finally...

## A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k

Li, Jerry; Schmidt, Ludwig
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.28%
Learning a Gaussian mixture model (GMM) is a fundamental problem in machine learning, learning theory, and statistics. One notion of learning a GMM is proper learning: here, the goal is to find a mixture of $k$ Gaussians $\mathcal{M}$ that is close to the density $f$ of the unknown distribution from which we draw samples. The distance between $\mathcal{M}$ and $f$ is typically measured in the total variation or $L_1$-norm. We give an algorithm for learning a mixture of $k$ univariate Gaussians that is nearly optimal for any fixed $k$. The sample complexity of our algorithm is $\tilde{O}(\frac{k}{\epsilon^2})$ and the running time is $(k \cdot \log\frac{1}{\epsilon})^{O(k^4)} + \tilde{O}(\frac{k}{\epsilon^2})$. It is well-known that this sample complexity is optimal (up to logarithmic factors), and it was already achieved by prior work. However, the best known time complexity for proper learning a $k$-GMM was $\tilde{O}(\frac{1}{\epsilon^{3k-1}})$. In particular, the dependence between $\frac{1}{\epsilon}$ and $k$ was exponential. We significantly improve this dependence by replacing the $\frac{1}{\epsilon}$ term with a $\log \frac{1}{\epsilon}$ while only increasing the exponent moderately. Hence, for any fixed $k$, the $\tilde{O} (\frac{k}{\epsilon^2})$ term dominates our running time...

## The Benefit of Multitask Representation Learning

Maurer, Andreas; Pontil, Massimiliano; Romera-Paredes, Bernardino
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.28%
We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning. Conditions on the theoretical advantage offered by multitask representation learning over independent tasks learning are established. In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality. Other potential applications of our results include multitask feature learning in reproducing kernel Hilbert spaces and multilayer, deep networks.; Comment: 28 pages, 8 figures

## Distribution-Independent Reliable Learning

Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.28%
We study several questions in the reliable agnostic learning framework of Kalai et al. (2009), which captures learning tasks in which one type of error is costlier than others. A positive reliable classifier is one that makes no false positive errors. The goal in the positive reliable agnostic framework is to output a hypothesis with the following properties: (i) its false positive error rate is at most $\epsilon$, (ii) its false negative error rate is at most $\epsilon$ more than that of the best positive reliable classifier from the class. A closely related notion is fully reliable agnostic learning, which considers partial classifiers that are allowed to predict "unknown" on some inputs. The best fully reliable partial classifier is one that makes no errors and minimizes the probability of predicting "unknown", and the goal in fully reliable learning is to output a hypothesis that is almost as good as the best fully reliable partial classifier from a class. For distribution-independent learning, the best known algorithms for PAC learning typically utilize polynomial threshold representations, while the state of the art agnostic learning algorithms use point-wise polynomial approximations. We show that one-sided polynomial approximations...

## IBSEAD: - A Self-Evolving Self-Obsessed Learning Algorithm for Machine Learning

Dundas, Jitesh; Chik, David
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.28%
We present IBSEAD or distributed autonomous entity systems based Interaction - a learning algorithm for the computer to self-evolve in a self-obsessed manner. This learning algorithm will present the computer to look at the internal and external environment in series of independent entities, which will interact with each other, with and/or without knowledge of the computer's brain. When a learning algorithm interacts, it does so by detecting and understanding the entities in the human algorithm. However, the problem with this approach is that the algorithm does not consider the interaction of the third party or unknown entities, which may be interacting with each other. These unknown entities in their interaction with the non-computer entities make an effect in the environment that influences the information and the behaviour of the computer brain. Such details and the ability to process the dynamic and unsettling nature of these interactions are absent in the current learning algorithm such as the decision tree learning algorithm. IBSEAD is able to evaluate and consider such algorithms and thus give us a better accuracy in simulation of the highly evolved nature of the human brain. Processes such as dreams, imagination and novelty...

## Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning

Oyen, Diane; Lane, Terran
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.28%
Bayesian network structure learning algorithms with limited data are being used in domains such as systems biology and neuroscience to gain insight into the underlying processes that produce observed data. Learning reliable networks from limited data is difficult, therefore transfer learning can improve the robustness of learned networks by leveraging data from related tasks. Existing transfer learning algorithms for Bayesian network structure learning give a single maximum a posteriori estimate of network models. Yet, many other models may be equally likely, and so a more informative result is provided by Bayesian structure discovery. Bayesian structure discovery algorithms estimate posterior probabilities of structural features, such as edges. We present transfer learning for Bayesian structure discovery which allows us to explore the shared and unique structural features among related tasks. Efficient computation requires that our transfer learning objective factors into local calculations, which we prove is given by a broad class of transfer biases. Theoretically, we show the efficiency of our approach. Empirically, we show that compared to single task learning, transfer learning is better able to positively identify true edges. We apply the method to whole-brain neuroimaging data.; Comment: 10 pages

## A Complete Characterization of Statistical Query Learning with Applications to Evolvability

Feldman, Vitaly
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.28%
Statistical query (SQ) learning model of Kearns (1993) is a natural restriction of the PAC learning model in which a learning algorithm is allowed to obtain estimates of statistical properties of the examples but cannot see the examples themselves. We describe a new and simple characterization of the query complexity of learning in the SQ learning model. Unlike the previously known bounds on SQ learning our characterization preserves the accuracy and the efficiency of learning. The preservation of accuracy implies that that our characterization gives the first characterization of SQ learning in the agnostic learning framework. The preservation of efficiency is achieved using a new boosting technique and allows us to derive a new approach to the design of evolutionary algorithms in Valiant's (2006) model of evolvability. We use this approach to demonstrate the existence of a large class of monotone evolutionary learning algorithms based on square loss performance estimation. These results differ significantly from the few known evolutionary algorithms and give evidence that evolvability in Valiant's model is a more versatile phenomenon than there had been previous reason to suspect.; Comment: Simplified Lemma 3.8 and it's applications

## Gaussian Processes for Data-Efficient Learning in Robotics and Control

Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.28%
Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.; Comment: 20 pages...

## Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Saxe, Andrew M.; McClelland, James L.; Ganguli, Surya
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
36.28%
Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output map, such networks have nonlinear gradient descent dynamics on weights that change with the addition of each new hidden layer. We show that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions. We provide an analytical description of these phenomena by finding new exact solutions to the nonlinear dynamics of deep learning. Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite, depth independent...

## A Mathematical Theory of Learning

Alabdulmohsin, Ibrahim
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
36.28%
In this paper, a mathematical theory of learning is proposed that has many parallels with information theory. We consider Vapnik's General Setting of Learning in which the learning process is defined to be the act of selecting a hypothesis in response to a given training set. Such hypothesis can, for example, be a decision boundary in classification, a set of centroids in clustering, or a set of frequent item-sets in association rule mining. Depending on the hypothesis space and how the final hypothesis is selected, we show that a learning process can be assigned a numeric score, called learning capacity, which is analogous to Shannon's channel capacity and satisfies similar interesting properties as well such as the data-processing inequality and the information-cannot-hurt inequality. In addition, learning capacity provides the tightest possible bound on the difference between true risk and empirical risk of the learning process for all loss functions that are parametrized by the chosen hypothesis. It is also shown that the notion of learning capacity equivalently quantifies how sensitive the choice of the final hypothesis is to a small perturbation in the training set. Consequently, algorithmic stability is both necessary and sufficient for generalization. While the theory does not rely on concentration inequalities...

## Statistical Mechanics of Nonlinear On-line Learning for Ensemble Teachers

Utsumi, Hideto; Miyoshi, Seiji; Okada, Masato
Tipo: Artigo de Revista Científica