Comunicação matemática - representações utilizadas pelos alunos

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ensino do 1.º e do 2.º Ciclo do Ensino Básico

Conhecimento do professor do 1º ciclo sobre números racionais

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção do grau de mestre em Educação Matemática na Educação Pré-escolar e nos 1º e 2º Ciclos do Ensino Básico

Estudo exploratório sobre as representações dos pais relativamente à educação em creche

Tendo em conta o aumento do número de estruturas de apoio à primeira infância, particularmente, a expansão da creche, a investigação tem-se debruçado sobre as questões da qualidade. A generalidade dos estudos centra-se na discriminação das dimensões de qualidade e, raramente, a representação dos pais sobre a creche (e sua qualidade) tem sido alvo de estudo. Partindo do pressuposto que a discussão sobre a qualidade da creche deve ser baseada na evidência empírica mas também é uma construção social baseada nos valores e representações dos seus atores, fomos ouvir os pais. Assim, quisemos conhecer: Como escolhiam a creche do seu filho(a)? Qual o seu conceito de qualidade? Que valor atribuem às experiências vividas pelos seus filhos ou filhas na creche? Que representação têm do papel do profissional de educação? Para o efeito, entrevistamos 20 pais sobre as suas Representações acerca da Creche num estudo exploratório e qualitativo. Os entrevistados foram na maioria dos casos mães (18 em 20) de crianças entre os 8 e os 32 meses (M = 21,65; 9 meninas, 11 meninos; 13 primogénitos). De modo geral, o estudo revelou que os pais valorizam a creche como espaço de promoção do desenvolvimento da criança; valorizam a dimensão afetiva do trabalho em creche; as educadoras como profissionais qualificados de educação e desejam uma relação estreita, aberta e respeitosa entre a creche e a família.

A visão e a voz do diretor: a construção do seu projeto de Intervenção

Dissertação apresentada para obtenção do grau de Mestre em Ciências da Educação - Área de especialização em Administração Escolar

Predictors of infant positive, negative and self-direct coping during face to face still-face in a Portuguese preterm sample

Past studies found three types of infant coping behaviour during Face-to-Face Still-Face paradigm (FFSF): a Positive Other-Directed Coping; a Negative Other-Directed Coping and a Self-Directed Coping. In the present study, we investigated whether those types of coping styles are predicted by: infants’ physiological responses; maternal representations of their infant’s temperament; maternal interactive behaviour in free play; and infant birth and medical status. The sample consisted of 46, healthy, prematurely born infants and their mothers. At one month, infant heart rate was collected in basal. At three months old (corrected age), infant heart-rate was registered during FFSF episodes. Mothers described their infants’ temperament using a validated Portuguese temperament scale, at infants three months of corrected age. As well, maternal interactive behaviour was evaluated during a free play situation using CARE-Index. Our findings indicate that positive coping behaviours were correlated with gestational birth weight, heart rate (HR), gestational age, and maternal sensitivity in free play. Gestational age and maternal sensitivity predicted Positive Other-Direct Coping behaviours. Moreover, Positive Other-Direct coping was negatively correlated with HR during Still-Face Episode. Self-directed behaviours were correlated with HR during Still-Face Episode and Recover Episode and with maternal controlling/intrusive behaviour. However, only maternal behaviour predicted Self-direct coping. Early social responses seem to be affected by infants’ birth status and by maternal interactive behaviour. Therefore, internal and external factors together contribute to infant ability to cope and to re-engage after stressful social events.

High-level Petri nets modules for embedded controllers design

Modular design is crucial to manage large-scale systems and to support the divide-and-conquer development approach. It allows hierarchical representations and, therefore, one can have a system overview, as well as observe component details. Petri nets are suitable to model concurrent systems, but lack on structuring mechanisms to support abstractions and the composition of sub-models, in particular when considering applications to embedded controllers design. In this paper we present a module construct, and an underlying high-level Petri net type, to model embedded controllers. Multiple interfaces can be declared in a module, thus, different instances of the same module can be used in different situations. The interface is a subset of the module nodes, through which the communication with the environment is made. Module places can be annotated with a generic type, overridden with a concrete type at instance level, and constants declared in a module may have a new value in each instance.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.