Formação de professores no programa nacional para a alfabetização na idade certa com uso das TIC: um estudo de caso no ensino fundamental

Dissertação de mestrado em Ciências da Educação (área de especialização em Tecnologia Educativa)

The reader : containing I. The art of delivery ... a selection of lessons in the various kinds of prose : II. Poetick numbers ... a selection of lessons in the various kinds of verse : being the third part of a Columbian exercise, the whole comprising an easy and systematical method of teaching and of learning the English language /

Mode of access: Internet.

Extending teaching and learning initiatives in the cross-disciplinary field of biotechnology

Our AUTC Biotechnology study (Phases 1 and 2) identified a range of areas that could benefit from a common approach by universities nationally. A national network of biotechnology educators needs to be solidified through more regular communication, biennial meetings, and development of methods for sharing effective teaching practices and industry placement strategies, for example. Our aims in this proposed study are to: a. Revisit the state of undergraduate biotechnology degree programs nationally to determine their rate of change in content, growth or shrinkage in student numbers (as the biotech industry has had its ups and downs in recent years), and sustainability within their institutions in light of career movements of key personnel, tightening budgets, and governmental funding priorities. b. Explore the feasibility of a range of initiatives to benefit university biotechnology education to determine factors such as how practical each one is, how much buy-in could be gained from potentially participating universities and industry counterparts, and how sustainable such efforts are. One of many such initiatives arising in our AUTC Biotech study was a national register of industry placements for final-year students. c. During scoping and feasibility study, to involve our colleagues who are teaching in biotechnology – and contributing disciplines. Their involvement is meant to yield not only meaningful insight into how to strengthen biotechnology teaching and learning but also to generate ‘buy-in’ on any initiatives that result from this effort.

Effects of age and alcohol exposure during early life on pyramidal cell numbers in the CA1-CA3 region of the rat hippocampus

We have previously shown that exposing rats to a relatively high dose of ethanol during early postnatal life can result in an alteration in spatial learning ability. The hippocampal formation is known to be involved in the control of this ability. The purpose of the present study was to determine whether exposure of rats to ethanol during early postnatal life had either immediate or delayed effects on the numbers of pyramidal cells in the CA1-CA3 subregion of the hippocampus. Wistar rats were exposed to a relatively high daily dose of ethanol at postnatal day 10-15 by placing them for 3 h/day in a chamber containing ethanol vapor. Groups of ethanol-treated (ET), separation control (SC), and mother-reared control (MRC) rats were anesthetized and killed at 16 and 30 days of age by perfusion with phosphate-buffered 2.5% glutaraldehyde. The Cavalieri principle was used to determine the volumes of the CA1 and CA2+CA3 regions. The physical disector method was used to estimate the numerical density of neurons in each of the subdivisions. The total number of pyramidal cells was calculated by multiplying the appropriate estimates of the numerical density by the volume. There were significant age-related reductions in the total numbers of pyramidal cells at 16-30 days of age irrespective of the groups examined. Ethanol treated rats were found to have slightly but significantly fewer pyramidal cell neurons than either the MRC or SC groups. These observations indicate that pyramidal cells in the hippocampus may be vulnerable to a relatively high dose of ethanol exposure during this short period of early postnatal life. (C) 2003 Wiley-Liss, Inc.

A bot spooler architecture to integrate virtual worlds with e-learning management systems for corporate training

Joining efforts of academic and corporate teams, we developed an integration architecture - MULTIS - that enables corporate e-learning managers to use a Learning Management System (LMS) for management of educational activities in virtual worlds. This architecture was then implemented for the Formare LMS. In this paper we present this architecture and concretizations of its implementation for the Second Life Grid/OpenSimulator virtual world platforms. Current systems are focused on activities managed by individual trainers, rather than groups of trainers and large numbers of trainees: they focus on providing the LMS with information about educational activities taking place in a virtual world and/or being able to access within the virtual world some of the information stored in the LMS, and disregard the streamlining of activity setup and data collection in multi-trainer contexts, among other administrative issues. This architecture aims to overcome the limitations of existing systems for organizational management of corporate e-learning activities.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Learning collaboration in General Internal Medicine: a pilot project for undergraduate students in nursing, physiotherapy and medicine

Background: A form of education called Interprofessional Education (IPE) occurs when two or more professions learn with, from and about each other. The purpose of IPE is to improve collaboration and the quality of care. Today, IPE is considered as a key educational approach for students in the health professions. IPE is highly effective when delivered in active patient care, such as in clinical placements. General internal medicine (GIM) is a core discipline where hospital-based clinical placements are mandatory for students in many health professions. However, few interprofessional (IP) clinical placements in GIM have been implemented. We designed such a placement. Placement design: The placement took place in the Department of Internal Medicine at the CHUV. It involved students from nursing, physiotherapy and medicine. The students were in their last year before graduation. Students formed teams consisting of one student from each profession. Each team worked in the same unit and had to take care of the same patient. The placement lasted three weeks. It included formal IP sessions, the most important being facilitated discussions or "briefings" (3x/w) during which the students discussed patient care and management. Four teams of students eventually took part in this project. Method: We performed a type of evaluation research called formative evaluation. This aimed at (1) understanding the educational experience and (2) assessing the impact of the placement on student learning. We collected quantitative data with pre-post clerkship questionnaires. We also collected qualitative data with two Focus Groups (FG) discussions at the end of the placement. The FG were audiotaped and transcribed. A thematic analysis was then performed. Results: We focused on the qualitative data, since the quantitative data lacked of statistical power due to the small numbers of students (N = 11). Five themes emerged from the FG analysis: (1) Learning of others' roles, (2) Learning collaborative competences, (3) Striking a balance between acquiring one's own professional competences and interprofessional competences, (4) Barriers to apply learnt IP competences in the future and (5) Advantages and disadvantages of IP briefings. Conclusions: Our IP clinical placement in GIM appeared to help students learn other professionals' roles and collaborative skills. Some challenges (e.g. finding the same patient for each team) were identified and will require adjustments.

Glucose-dependent insulinotropic polypeptide receptor knockout mice are impaired in learning, synaptic plasticity, and neurogenesis.

Glucose-dependent insulinotropic polypeptide (GIP) is a key incretin hormone, released from intestine after a meal, producing a glucose-dependent insulin secretion. The GIP receptor (GIPR) is expressed on pyramidal neurons in the cortex and hippocampus, and GIP is synthesized in a subset of neurons in the brain. However, the role of the GIPR in neuronal signaling is not clear. In this study, we used a mouse strain with GIPR gene deletion (GIPR KO) to elucidate the role of the GIPR in neuronal communication and brain function. Compared with C57BL/6 control mice, GIPR KO mice displayed higher locomotor activity in an open-field task. Impairment of recognition and spatial learning and memory of GIPR KO mice were found in the object recognition task and a spatial water maze task, respectively. In an object location task, no impairment was found. GIPR KO mice also showed impaired synaptic plasticity in paired-pulse facilitation and a block of long-term potentiation in area CA1 of the hippocampus. Moreover, a large decrease in the number of neuronal progenitor cells was found in the dentate gyrus of transgenic mice, although the numbers of young neurons was not changed. Together the results suggest that GIP receptors play an important role in cognition, neurotransmission, and cell proliferation.

Deep learning of representations and its application to computer vision

L’objectif de cette thèse par articles est de présenter modestement quelques étapes du parcours qui mènera (on espère) à une solution générale du problème de l’intelligence artificielle. Cette thèse contient quatre articles qui présentent chacun une différente nouvelle méthode d’inférence perceptive en utilisant l’apprentissage machine et, plus particulièrement, les réseaux neuronaux profonds. Chacun de ces documents met en évidence l’utilité de sa méthode proposée dans le cadre d’une tâche de vision par ordinateur. Ces méthodes sont applicables dans un contexte plus général, et dans certains cas elles on tété appliquées ailleurs, mais ceci ne sera pas abordé dans le contexte de cette de thèse. Dans le premier article, nous présentons deux nouveaux algorithmes d’inférence variationelle pour le modèle génératif d’images appelé codage parcimonieux “spike- and-slab” (CPSS). Ces méthodes d’inférence plus rapides nous permettent d’utiliser des modèles CPSS de tailles beaucoup plus grandes qu’auparavant. Nous démontrons qu’elles sont meilleures pour extraire des détecteur de caractéristiques quand très peu d’exemples étiquetés sont disponibles pour l’entraînement. Partant d’un modèle CPSS, nous construisons ensuite une architecture profonde, la machine de Boltzmann profonde partiellement dirigée (MBP-PD). Ce modèle a été conçu de manière à simplifier d’entraînement des machines de Boltzmann profondes qui nécessitent normalement une phase de pré-entraînement glouton pour chaque couche. Ce problème est réglé dans une certaine mesure, mais le coût d’inférence dans le nouveau modèle est relativement trop élevé pour permettre de l’utiliser de manière pratique. Dans le deuxième article, nous revenons au problème d’entraînement joint de machines de Boltzmann profondes. Cette fois, au lieu de changer de famille de modèles, nous introduisons un nouveau critère d’entraînement qui donne naissance aux machines de Boltzmann profondes à multiples prédictions (MBP-MP). Les MBP-MP sont entraînables en une seule étape et ont un meilleur taux de succès en classification que les MBP classiques. Elles s’entraînent aussi avec des méthodes variationelles standard au lieu de nécessiter un classificateur discriminant pour obtenir un bon taux de succès en classification. Par contre, un des inconvénients de tels modèles est leur incapacité de générer deséchantillons, mais ceci n’est pas trop grave puisque la performance de classification des machines de Boltzmann profondes n’est plus une priorité étant donné les dernières avancées en apprentissage supervisé. Malgré cela, les MBP-MP demeurent intéressantes parce qu’elles sont capable d’accomplir certaines tâches que des modèles purement supervisés ne peuvent pas faire, telles que celle de classifier des données incomplètes ou encore celle de combler intelligemment l’information manquante dans ces données incomplètes. Le travail présenté dans cette thèse s’est déroulé au milieu d’une période de transformations importantes du domaine de l’apprentissage à réseaux neuronaux profonds qui a été déclenchée par la découverte de l’algorithme de “dropout” par Geoffrey Hinton. Dropout rend possible un entraînement purement supervisé d’architectures de propagation unidirectionnel sans être exposé au danger de sur- entraînement. Le troisième article présenté dans cette thèse introduit une nouvelle fonction d’activation spécialement con ̧cue pour aller avec l’algorithme de Dropout. Cette fonction d’activation, appelée maxout, permet l’utilisation de aggrégation multi-canal dans un contexte d’apprentissage purement supervisé. Nous démontrons comment plusieurs tâches de reconnaissance d’objets sont mieux accomplies par l’utilisation de maxout. Pour terminer, sont présentons un vrai cas d’utilisation dans l’industrie pour la transcription d’adresses de maisons à plusieurs chiffres. En combinant maxout avec une nouvelle sorte de couche de sortie pour des réseaux neuronaux de convolution, nous démontrons qu’il est possible d’atteindre un taux de succès comparable à celui des humains sur un ensemble de données coriace constitué de photos prises par les voitures de Google. Ce système a été déployé avec succès chez Google pour lire environ cent million d’adresses de maisons.

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks

Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. Often the parameters used in these networks need to be learned from examples. Unfortunately, estimating the parameters via exact probabilistic calculations (i.e, the EM-algorithm) is intractable even for networks with fairly small numbers of hidden units. We propose to avoid the infeasibility of the E step by bounding likelihoods instead of computing them exactly. We introduce extended and complementary representations for these networks and show that the estimation of the network parameters can be made fast (reduced to quadratic optimization) by performing the estimation in either of the alternative domains. The complementary networks can be used for continuous density estimation as well.

Efficiency and Equity in Learning Communities and schools

Traditionally, school efficiency has been measured as a function of educational production. In the last two decades, however, studies in the economics of education have indicated that more is required to improve school efficiency: researchers must explore how significant changes in school organization affect the performance of at-risk students. In this paper we introduce Henry Levin’s adoption of the X-efficiency approach to education and we describe the efficient and cost-effective characteristics of one Learning Communities Project School that significantly improved its student outcomes and enrollment numbers and reduced its absenteeism rate to zero. The organizational change that facilitated these improvements defined specific issues to address. Students’ school success became the focus of the school project, which also offered specific incentives, selected teachers, involved parents and community members in decisions, and used the most efficient technologies and methods. This case analysis reveals new two elements—family training and community involvement—that were not explicit parts of Levin’s adaptation. The case of the Antonio Machado Public School should attract the attention of both social scientists and policy makers

Dynamic automata for mobile robot learning

Researchers at the University of Reading have developed over many years some simple mobile robots that explore an environment they perceive through simple ultrasonic sensors. Information from these sensors has allowed the robots to learn the simple task of moving around while avoiding dynamic obstacles using a static set of fuzzy automata, the choice of which has been criticised, due to its arbitrary nature. This paper considers how a dynamic set of automata can overcome this criticism. In addition, a new reinforcement learning function is outlined which is both scalable to different numbers and types of sensors. The innovations compare successfully with earlier work.

Scanlation - What Fan Translators of Manga Learn in the Informal Learning Environment

The present paper discusses two pilot studies carried out to see the possibility of the fan community of manga (Japanese comics), in which fan translators translate the original Japanese manga into English (which is called scanlation), functioning as an informal learning environment for the Japanese language learning and translator training. Two pilot studies consist of a) comparison of the original Japanese version with the scanlation and official translation, and b) comparison of the original Japanese version with two different versions of scanlation to see the translators’ level of Japanese language and the overall translation quality. The results show that in scanlation versions, there were numbers of inaccuracies which would prevent them to be treated as professional translation. Some of these errors are clearly caused by insufficient understanding of Japanese language by the translator. However, the pilot studies also suggested some interesting features of fan translation, such as the treatment of cultural references. The two pilot studies indicate that it is desirable to conduct further studies with more data, in order to confirm the results of present studies, and to see the possible relationship between the types of trnalsation errors found in scanlation and the particular type of Japanese language (informal, conversational) that could be learned from manga.

Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.