Learning by doing e curva de aprendizagem. Uma aplicação na esfera da Microeconomia

Trabalho apresentado no âmbito dos artigos 11º e 14º do Regulamento de Prestação de Serviço Docente do ISCAL

Learning by doing e Curva de aprendizagem. Um estudo empírico aplicado em Microeconomia

Trabalho apresentado no âmbito dos artigos 11º e 14º do Regulamento de Prestação de Serviço Docente do ISCAL

O desenvolvimento de um modelo de ensino virtual num contexto de investimento incipiente em e - learning: progressos e desafios

Este artigo relata o desenvolvimento de um modelo de ensino virtual em curso na Universidade dos Açores. Depois de ter sido adotado na lecionação de disciplinas da área da Teoria e Desenvolvimento Curricular em regime de e-learning e b-learning, o modelo foi, no ano académico de 2014/15, estendido à lecionação de outras disciplinas. Além de descrever o modelo e explicar a sua evolução, o artigo destaca a sua adoção no contexto particular de uma disciplina cuja componente online foi lecionada em circunstâncias especialmente desafiadoras. Neste sentido, explica o processo de avaliação da experiência, discute os seus resultados e sugere pistas de melhoria. Essa avaliação enquadra-se num processo de investigação do design curricular – a metodologia que tem sido usada para estudar o desenvolvimento do modelo.

Psychological well-being and perception of disease severity in people with multiple sclerosis, who underwent a program of self-regulation to promote physical activity

Rehabilitation is very important for in the results of treatment in individuals with multiple sclerosis. Rehabilitation processes occur through gradual changes. These changes integrate intrinsic and extrinsic mechanisms of the individual, promoting adaptations to the needs and activities of daily living according to individual goals. Recommendations for exercise in multiple sclerosis: these recommendations apply only to patients with EDSS less than 7; moderate intensity aerobic exercise for a total of 20 to 30 minutes, twice or three times for week; the resistance training with low or moderate intensity is well tolerated by patients with MS; associated with these exercises were recommended flexibility exercises of moderate intensity, as well as strengthening exercises. The aim of this study is to examine the implications of the program of self-regulation in the perception of illness and mental health (psychological well-being domain) in multiple sclerosis patients.

Importance of program physical activity in quality of life in multiple sclerosis patients, six months after the intervention

Background: Multiple Sclerosis (MS) is a chronic disease of the central nervous system that affects more often young adults in the prime of his career and personal development, with no cure and unknown causes. The most common signs and symptoms are fatigue, muscle weakness, changes in sensation, ataxia, changes in balance, gait difficulties, memory difficulties, cognitive impairment and difficulties in problem solving MS is a relatively common neurological disorder in which various impairments and disabilities impact strongly on function and daily life activities. Purpose: The aim of this study is to examine the implications of an Intervention Program of Physical Activity (IPPA) in quality of life in MS patients, six months after the intervention.

Evidence based for exercise program in physical therapy in patients with multiple sclerosis

Background: Multiple sclerosis is a disease of the central nervous system that affects more frequently young women. It is a progressive and unpredictable disease, resulting in some cases of disabilities and limitations to physical, psychological and social level. Purpose: To review the literature for evidence based of the effectiveness of physiotherapy intervention in multiple sclerosis.

Enabling Global Experiments With Interactive Reconfiguration And Steering By Multiple Users

In global scientific experiments with collaborative scenarios involving multinational teams there are big challenges related to data access, namely data movements are precluded to other regions or Clouds due to the constraints on latency costs, data privacy and data ownership. Furthermore, each site is processing local data sets using specialized algorithms and producing intermediate results that are helpful as inputs to applications running on remote sites. This paper shows how to model such collaborative scenarios as a scientific workflow implemented with AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic), a decentralized framework offering a feasible solution to run the workflow activities on distributed data centers in different regions without the need of large data movements. The AWARD workflow activities are independently monitored and dynamically reconfigured and steering by different users, namely by hot-swapping the algorithms to enhance the computation results or by changing the workflow structure to support feedback dependencies where an activity receives feedback output from a successor activity. A real implementation of one practical scenario and its execution on multiple data centers of the Amazon Cloud is presented including experimental results with steering by multiple users.

The extended unsymmetric frontal solution for multiple-point constraints

The purpose of this paper is to discuss the linear solution of equality constrained problems by using the Frontal solution method without explicit assembling. Design/methodology/approach - Re-written frontal solution method with a priori pivot and front sequence. OpenMP parallelization, nearly linear (in elimination and substitution) up to 40 threads. Constraints enforced at the local assembling stage. Findings - When compared with both standard sparse solvers and classical frontal implementations, memory requirements and code size are significantly reduced. Research limitations/implications - Large, non-linear problems with constraints typically make use of the Newton method with Lagrange multipliers. In the context of the solution of problems with large number of constraints, the matrix transformation methods (MTM) are often more cost-effective. The paper presents a complete solution, with topological ordering, for this problem. Practical implications - A complete software package in Fortran 2003 is described. Examples of clique-based problems are shown with large systems solved in core. Social implications - More realistic non-linear problems can be solved with this Frontal code at the core of the Newton method. Originality/value - Use of topological ordering of constraints. A-priori pivot and front sequences. No need for symbolic assembling. Constraints treated at the core of the Frontal solver. Use of OpenMP in the main Frontal loop, now quantified. Availability of Software.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Machine learning Vasicek model calibration with Gaussian processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.

Desenho universal para a aprendizagem: construindo práticas pedagógicas inclusivas

O presente artigo tem como objetivo apresentar uma grelha de planificação da intervenção facilitadora do desenvolvimento de práticas pedagógicas inclusivas. Para o efeito, procedeu-se a uma revisão da literatura centrada nos conceitos de Educação Inclusiva e de Desenho Universal para a Aprendizagem (Universal Design for Learning), a qual permitiu identificar e fundamentar a pertinência das dimensões a considerar na planificação da intervenção pedagógica, de modo a assegurar o acesso, a participação e o sucesso de todos os alunos. Com a apresentação da grelha de planificação da intervenção pedagógica pretende-se, em última análise, sublinhar a necessidade e a importância de desenvolver processos de planificação que disponibilizem formas diversificadas de motivação e envolvimento dos alunos, que equacionem múltiplos processos de apresentação dos conteúdos a aprender e, por último, que possibilitem a utilização de diversas formas de ação e expressão por parte dos alunos.

Moxtra ‘o meu’, ‘o teu’, ‘o nosso’, ‘o vosso’ mobile learning

Neste workshop pretende-se apresentar uma aplicação móvel (Moxtra) que integra uma experiência de inovação pedagógica no âmbito do mobile-learning que está em pleno desenvolvimento, com a participação ativa dos estudantes e docentes das unidades curriculares de Hematologia Laboratorial I e II do curso de Ciências Biomédicas Laboratoriais. A adesão dos estudantes ao projeto mobile-learning é inédita no nosso país e tem sido muito positiva. O workshop terá dois objetivos: a) Conhecer os principais atributos da aplicação Moxtra; b) Construir um modelo de gestão de aprendizagem para uma unidade curricular.

Curso E-learning de sistemas multicorpo (multibody systems) para engenheiros mecânicos

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Respostas educativas para alunos com multideficiência: conceções e práticas

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Especial