Trabalho docente e a emergência da imaginação e da criatividade na aprendizagem : possibilidades e desafios

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Educação, Programa de Pós-Graduação em Educação, 2016.

O trabalho científico em ambiente de aprendizagem cooperativa no 5º ano de escolaridade: análise do ensino e da aprendizagem

Dissertação de mest., Unidade de Ciências Exactas e Humanas, Escola Superior de Educação, Univ. do Algarve, 1998

L’interaction comme méthode d’apprentissage du Français langue étrangère (FLE) en Suède

Mon travail s’inspire de l’idée de la Direction nationale de l’enseignement scolaire suédoise (Skolverket, 2011) qui suggère que « les élèves doivent avoir la possibilité de développer une capacité de communication et de compréhension de la langue ». Mon mémoire commence par une introduction générale, continue avec une analyse des manuels utilisés dans l’apprentissage du FLE et des activités favorisant l’interaction - échange réciproque verbale ou non verbale, pour terminer par une conclusion générale. Comme il n’existe pas d’enseignement sans méthodes appropriées, il existe des manuels qu’on peut utiliser afin de faciliter l’apprentissage par l’interaction de la langue cible. Les activités de communication orale et écrite d’ordre pédagogique sont indispensables. Tout comme l’écrit Revue française de pédagogie (1994 :133) « La classe est un système social complexe dont les parties sont en interaction dynamique, acteurs (enseignant et élève[s]), situation, matière selon des statuts sociaux. ». Pour effectuer mes recherches, j’ai porté mon attention sur l’analyse des manuels scolaires qu’il m’a été permis de consulter. Il s’agit des manuels d’entraide comme : Mais oui 3 et Escalade littéraire. Concernant l’analyse du matériel didactique favorisant l’interaction, ces deux manuels proposent des exercices de réflexion selon le niveau de l’apprenant. Dans mon travail, je m’intéresse à l’aspect didactique et linguistique de ces deux manuels scolaires.

Neural states in parietal areas during arm reaching

Since the first subdivisions of the brain into macro regions, it has always been thought a priori that, given the heterogeneity of neurons, different areas host specific functions and process unique information in order to generate a behaviour. Moreover, the various sensory inputs coming from different sources (eye, skin, proprioception) flow from one macro area to another, being constantly computed and updated. Therefore, especially for non-contiguous cortical areas, it is not expected to find the same information. From this point of view, it would be inconceivable that the motor and the parietal cortices, diversified by the information encoded and by the anatomical position in the brain, could show very similar neural dynamics. With the present thesis, by analyzing the population activity of parietal areas V6A and PEc with machine learning methods, we argue that a simplified view of the brain organization do not reflect the actual neural processes. We reliably detected a number of neural states that were tightly linked to distinct periods of the task sequence, i.e. the planning and execution of movement and the holding of target as already observed in motor cortices. The states before and after the movement could be further segmented into two states related to different stages of movement planning and arm posture processing. Rather unexpectedly, we found that activity during the movement could be parsed into two states of equal duration temporally linked to the acceleration and deceleration phases of the arm. Our findings suggest that, at least during arm reaching in 3D space, the posterior parietal cortex (PPC) shows low-level population neural dynamics remarkably similar to those found in the motor cortices. In addition, the present findings suggest that computational processes in PPC could be better understood if studied using a dynamical system approach rather than studying a mosaic of single units.

Hybrid Artificial Intelligence to Extract Patterns and Rules from Argumentative and Legal Texts

This Thesis is composed of a collection of works written in the period 2019-2022, whose aim is to find methodologies of Artificial Intelligence (AI) and Machine Learning to detect and classify patterns and rules in argumentative and legal texts. We define our approach “hybrid”, since we aimed at designing hybrid combinations of symbolic and sub-symbolic AI, involving both “top-down” structured knowledge and “bottom-up” data-driven knowledge. A first group of works is dedicated to the classification of argumentative patterns. Following the Waltonian model of argument and the related theory of Argumentation Schemes, these works focused on the detection of argumentative support and opposition, showing that argumentative evidences can be classified at fine-grained levels without resorting to highly engineered features. To show this, our methods involved not only traditional approaches such as TFIDF, but also some novel methods based on Tree Kernel algorithms. After the encouraging results of this first phase, we explored the use of a some emerging methodologies promoted by actors like Google, which have deeply changed NLP since 2018-19 — i.e., Transfer Learning and language models. These new methodologies markedly improved our previous results, providing us with best-performing NLP tools. Using Transfer Learning, we also performed a Sequence Labelling task to recognize the exact span of argumentative components (i.e., claims and premises), thus connecting portions of natural language to portions of arguments (i.e., to the logical-inferential dimension). The last part of our work was finally dedicated to the employment of Transfer Learning methods for the detection of rules and deontic modalities. In this case, we explored a hybrid approach which combines structured knowledge coming from two LegalXML formats (i.e., Akoma Ntoso and LegalRuleML) with sub-symbolic knowledge coming from pre-trained (and then fine-tuned) neural architectures.

Data sensitivity detection in chat interactions for privacy protection

In recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’). This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model? RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential of hybrid architectures in solving automatic tasks.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

Robotic grasping: State of the art and modular real-world implementation

Robotic Grasping is an important research topic in robotics since for robots to attain more general-purpose utility, grasping is a necessary skill, but very challenging to master. In general the robots may use their perception abilities like an image from a camera to identify grasps for a given object usually unknown. A grasp describes how a robotic end-effector need to be positioned to securely grab an object and successfully lift it without lost it, at the moment state of the arts solutions are still far behind humans. In the last 5–10 years, deep learning methods take the scene to overcome classical problem like the arduous and time-consuming approach to form a task-specific algorithm analytically. In this thesis are present the progress and the approaches in the robotic grasping field and the potential of the deep learning methods in robotic grasping. Based on that, an implementation of a Convolutional Neural Network (CNN) as a starting point for generation of a grasp pose from camera view has been implemented inside a ROS environment. The developed technologies have been integrated into a pick-and-place application for a Panda robot from Franka Emika. The application includes various features related to object detection and selection. Additionally, the features have been kept as generic as possible to allow for easy replacement or removal if needed, without losing time for improvement or new testing.

A shift from passive teaching at medical conferences to more interactive methods improves physician learning

Conferences that deliver interactive sessions designed to enhance physician participation, such as role play, small discussion groups, workshops, hands-on training, problem- or case-based learning and individualised training sessions, are effective for physician education.

Special issue on methods and learning in functional MRI: Introductory remarks

This special issue represents a further exploration of some issues raised at a symposium entitled “Functional magnetic resonance imaging: From methods to madness” presented during the 15th annual Theoretical and Experimental Neuropsychology (TENNET XV) meeting in Montreal, Canada in June, 2004. The special issue’s theme is methods and learning in functional magnetic resonance imaging (fMRI), and it comprises 6 articles (3 reviews and 3 empirical studies). The first (Amaro and Barker) provides a beginners guide to fMRI and the BOLD effect (perhaps an alternative title might have been “fMRI for dummies”). While fMRI is now commonplace, there are still researchers who have yet to employ it as an experimental method and need some basic questions answered before they venture into new territory. This article should serve them well. A key issue of interest at the symposium was how fMRI could be used to elucidate cerebral mechanisms responsible for new learning. The next 4 articles address this directly, with the first (Little and Thulborn) an overview of data from fMRI studies of category-learning, and the second from the same laboratory (Little, Shin, Siscol, and Thulborn) an empirical investigation of changes in brain activity occurring across different stages of learning. While a role for medial temporal lobe (MTL) structures in episodic memory encoding has been acknowledged for some time, the different experimental tasks and stimuli employed across neuroimaging studies have not surprisingly produced conflicting data in terms of the precise subregion(s) involved. The next paper (Parsons, Haut, Lemieux, Moran, and Leach) addresses this by examining effects of stimulus modality during verbal memory encoding. Typically, BOLD fMRI studies of learning are conducted over short time scales, however, the fourth paper in this series (Olson, Rao, Moore, Wang, Detre, and Aguirre) describes an empirical investigation of learning occurring over a longer than usual period, achieving this by employing a relatively novel technique called perfusion fMRI. This technique shows considerable promise for future studies. The final article in this special issue (de Zubicaray) represents a departure from the more familiar cognitive neuroscience applications of fMRI, instead describing how neuroimaging studies might be conducted to both inform and constrain information processing models of cognition.

Análisis comparativo entre actividades de aprendizaje diseñadas con Estudio de Casos y Aprendizaje Basado en Problemas, y actividades de tipo tradicional, como dispositivos didácticos en Educación a Distancia. Comparative analysis of learning activities designed with Problem Based Learning and Case Study approaches, and activities designed with traditional methods, in Distance Education.

Identificación y caracterización del problema: El problema que guía este proyecto, pretende dar respuesta a interrogantes tales como: ¿De qué modo el tipo de actividades que se diseñan, se constituyen en dispositivos posibilitadores de la comprensión de los temas propios de cada asignatura, por parte de los alumnos? A partir de esta pregunta, surge la siguiente: Al momento de resolver las actividades, ¿qué estrategias cognitivas ponen en juego los estudiantes? y ¿cuáles de ellas favorecen procesos de construcción del conocimiento? Hipótesis: - Las asignaturas cuyas actividades están elaboradas bajo la metodología de Aprendizaje Basado en Problemas y Estudio de Casos, propician aprendizajes significativos por parte de los estudiantes. - Las actividades elaboradas bajo la metodología del Aprendizaje Basado en Problemas y el Estudio de Casos requieren de procesos cognitivos más complejos que los que se implementan en las de tipo tradicional. Objetivo: - Identificar el impacto que tienen las actividades de aprendizaje de tipo tradicional y las elaboradas bajo la metodología de Aprendizaje Basado en Problemas y Estudio de Casos, en el aprendizaje de los alumnos. Materiales y Métodos: a) Análisis de las actividades de aprendizaje del primero y segundo año de la carrera de Abogacía, bajo lamodalidad a Distancia. b) Entrevistas tanto a docentes contenidistas como así también a los tutores. c) Encuestas y entrevistas a los alumnos. Resultados esperados: Se pretende confirmar que las actividades de aprendizaje, diseñadas bajo la metodología del Aprendizaje Basado en Problemas y el Estudio de Casos, promueven aprendizajes significativos en los alumnos. Importancia del proyecto y pertinencia: La relevancia del presente proyecto se podría identificar a través de dos grandes variables vinculadas entre sí: la relacionada con el dispositivo didáctico (estrategias implementadas por los alumnos) y la referida a lo institucional (carácter innovador de la propuesta de enseñanza y posibilidad de extenderla a otras cátedras). El presente proyecto pretende implementar mejoras en el diseño de las actividades de aprendizaje, a fin de promover en los alumnos la generación de ideas y soluciones responsables y el desarrollo de su capacidad analítica y reflexiva.

Eliciting and fostering learners' metacognitive knowledge about language learning in self-directed learning programs: a review of data collection methods and procedures

Són molts els estudis que avui en dia incideixen en la necessitat d’oferir un suport metodològic i psicològic als aprenents que treballen de manera autònoma. L’objectiu d’aquest suport és ajudar-los a desenvolupar les destreses que necessiten per dirigir el seu aprenentatge així com una actitud positiva i una major conscienciació envers aquest aprenentatge. En definitiva, aquests dos tipus de preparació es consideren essencials per ajudar els aprenents a esdevenir més autònoms i més eficients en el seu propi aprenentatge. Malgrat això, si bé és freqüent trobar estudis que exemplifiquen aplicacions del suport metodològic dins els seus programes, principalment en la formació d’estratègies o ajudant els aprenents a desenvolupar un pla de treball, aquest no és el cas quan es tracta de la seva preparació psicològica. Amb rares excepcions, trobem estudis que documentin com s’incideix en les actituds i en les creences dels aprenents, també coneguts com a coneixement metacognitiu (CM), en programes que fomenten l’autonomia en l’aprenentatge. Els objectius d’aquest treball son dos: a) oferir una revisió d’estudis que han utilitzat diferents mitjans per incidir en el CM dels aprenents i b) descriure les febleses i avantatges dels procediments i instruments que utilitzen, tal com han estat valorats en estudis de recerca, ja que ens permetrà establir criteris objectius sobre com i quan utilitzar-los en programes que fomentin l’aprenentatge autodirigit.

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task