In recent years, many machine learning algorithms have been developed and widely applied in various applications. However, most of them have considered the data distributions of the training and test datasets to be similar. This thesis concerns on the decrease of generalization ability in a test dataset when the data distribution is different from that of the training dataset. As labels may be unavailable in the test dataset in practical applications, we follow the effective approach of unsupervised domain adaptation and propose distribution alignment methods to improve the generalization ability of models learned from the training dataset in the test dataset. To solve the problem of joint distribution alignment without target labels, we propose a new criterion of domain-shared group sparsity that is an equivalent condition for equal conditional distribution. A domain-shared group-sparse dictionary learning model is built with the proposed criterion, and a cross-domain label propagation method is developed to learn a target-domain classifier using the domain-shared group-sparse representations and the target-specific information from the target data. Experimental results show that the proposed method achieves good performance on cross-domain face and object recognition. Moreover, most distribution alignment methods have not considered the difference in distribution structures, which results in insufficient alignment across domains. Therefore, a novel graph alignment method is proposed, which aligns both data representations and distribution structural information across the source and target domains. An adversarial network is developed for graph alignment by mapping both source and target data to a feature space where the data are distributed with unified structure criteria. Promising results have been obtained in the experiments on cross-dataset digit and object recognition. Problem of dataset bias also exists in human pose estimation across datasets with different image qualities. Thus, this thesis proposes to synthesize target body parts for cross-domain distribution alignment, to address the problem of cross-quality pose estimation. A translative dictionary is learned to associate the source and target domains, and a cross-quality adaptation model is developed to refine the source pose estimator using the synthesized target body parts. We perform cross-quality experiments on three datasets with different image quality using two state-of-the-art pose estimators, and compare the proposed method with five unsupervised domain adaptation methods. Our experimental results show that the proposed method outperforms not only the source pose estimators, but also other unsupervised domain adaptation methods.

This study investigated the effectiveness of two methods of instruction, Teacher-Based Instruction (TBI) versus Computer-Assisted Language Learning (CALL), in improving undergraduate Arab learners' English reading ability in the three aspects of speed, comprehension and vocabulary knowledge. The Experimental Pre-test/Post-test Treatment Group Design was implemented in both experiments carried out in this study. Two samples of 100 and 150 students for Experiments I & II, respectively, were randomly chosen from two higher education institutions in the Arab world. Each sample was divided into two groups depending on learners' pre-instruction preferences for TBI or CALL methods. After eight weeks of instruction using one method, the students of the two groups exchanged instructional methods for another period of eight weeks. In both cases the learners attended three 90- minute reading lectures per week. Quantitative and qualitative data analysis showed that CALL was significantly more effective than TBI for improving the learners' reading ability in the three aspects targeted. Results showed that CALL was more effective due to different reasons: learners were more motivated to read and they enjoyed reading; CALL made learners' reading progress visible to them through immediate feedback; it fostered learner autonomy and their desire to be in control of the program and it offered the learners a large number of different reading activities to work on. Learners' suggestions for improving CALL were mainly related to increasing the time they can use CALL programs in learning to read. Findings of the study should encourage higher education institutions especially in the Arab world to take considerable steps towards utilising computers in instruction. Even at school level this utilisation should be considered, but further research should be carried out with learners at different age levels and in different regions in the Arab world.

Testing analog circuits is more difficult than digital circuits. The reasons for this difficulty include continuous time and amplitude signals, lack of well-accepted testing techniques and time and cost required for its realization. The traditional method for testing analog circuits involves measuring all the performance parameters and comparing the measured parameters with the limits of the data-sheet specifications. Because of the large number of data-sheet specifications, the test generation and application requires long test times and expensive test equipment. This thesis proposes an implicit functional testing technique for analog circuits that can be easily implemented in BIST circuitry. The proposed technique does not require measuring data-sheet performance parameters. To simplify the testing only time domain digital input is required. For each circuit under test (CUT) a cross-covariance signature is computed from the test input and CUT's output. The proposed method requires a training sample of the CUT to be binned to the data-sheet specifications. The binned CUT sample cross-covariance signatures are mapped with a supervised machine learning classifier. For each bin, the classifiers select unique sub-sets of the cross-covariance signature. The trained classifier is then used to bin newly manufactured copies of the CUT. The proposed technique is evaluated on synthetic data generated from the Monte Carlo simulation of the nominal circuit. Results show the machine learning classifier must be chosen to match the imbalanced bin populations common in analog circuit testing. For sample sizes of 700+ and training for individual bins, classifier test escape rates ranged from 1000 DPM to 10,000 DPM.

No description available.

State-of-the-art machine learning methods such as convolutional neural networks (CNNs) are frequently employed in computer vision. Despite their high performance on unseen data, CNNs are often criticized for lacking transparency — that is, providing very limited if any information about the internal decision-making process. In some applications, especially in healthcare, such transparency of algorithms is crucial for end users, as trust in diagnosis and prognosis is important not only for the satisfaction and potential adherence of patients, but also for their health. Explainable artificial intelligence (XAI) aims to open up this “black box,” often perceived as a cryptic and inconceivable algorithm, to increase understanding of the machines’ reasoning.XAI is an emerging field, and techniques for making machine learning explainable are becoming increasingly available. XAI for computer vision mainly focuses on image classification, whereas interpretability in other tasks remains challenging. Here, I examine explainability in computer vision beyond image classification, namely in semantic segmentation and 3D multitarget image regression. This thesis consists of five chapters. In Chapter 1 (Introduction), the background of artificial intelligence (AI), XAI, computer vision, and optics is presented, and the definitions of the terminology for XAI are proposed. Chapter 2 is focused on explaining the predictions of U-Net, a CNN commonly used for semantic image segmentation, and variations of this architecture. To this end, I propose the gradient-weighted class activation mapping for segmentation (Seg-Grad-CAM) method based on the well-known Grad-CAM method for explainable image classification. In Chapter 3, I present the application of deep learning to estimation of optical aberrations in microscopy biodata by identifying the present Zernike aberration modes and their amplitudes. A CNN-based approach PhaseNet can accurately estimate monochromatic aberrations in images of point light sources. I extend this method to objects of complex shapes. In Chapter 4, an approach for explainable 3D multitarget image regression is reported. First, I visualize how the model differentiates the aberration modes using the local interpretable model-agnostic explanations (LIME) method adapted for 3D image classification. Then I “explain,” using LIME modified for multitarget 3D image regression (Image-Reg-LIME), the outputs of the regression model for estimation of the amplitudes. In Chapter 5, the results are discussed in a broader context. The contribution of this thesis is the development of explainability methods for semantic segmentation and 3D multitarget image regression of optical aberrations. The research opens the door for further enhancement of AI’s transparency.:Title Page i List of Figures xi List of Tables xv 1 Introduction 1 1.1 Essential Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Artificial intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Explainable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.3 Proposed definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Explainable Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 Aims and applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.2 Image classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.3 Image regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3.4 Image segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 Optics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4.1 Aberrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.2 Zernike polynomials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.5 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.5.2 Dissertation outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2 Explainable Image Segmentation 23 2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.1 CAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.2 Grad-CAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.3 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.4 Seg-Grad-CAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.4.1 Circles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.4.2 TextureMNIST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.4.3 Cityscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.5.1 Circles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.5.2 TextureMNIST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.5.3 Cityscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.6 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3 Estimation of Aberrations 55 3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.3.1 PhaseNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.3.2 PhaseNet data generator . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.3.3 Retrieval of noise parameters . . . . . . . . . . . . . . . . . . . . . . . . 62 3.3.4 Data generator with phantoms . . . . . . . . . . . . . . . . . . . . . . . 62 3.3.5 Restoration via deconvolution . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.6 Convolution with the “zero” synthetic PSF . . . . . . . . . . . . . . . . 63 3.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.4.1 Astrocytes (synthetic data) . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.4.2 Fluorescent beads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.4.3 Drosophila embryo (live sample) . . . . . . . . . . . . . . . . . . . . . . 67 3.4.4 Neurons (fixed sample) . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.5.1 Astrocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.5.2 Conclusions on the results for astrocytes . . . . . . . . . . . . . . . . . . 74 3.5.3 Fluorescent beads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 3.5.4 Conclusions on the results for fluorescent beads . . . . . . . . . . . . . . 81 3.5.5 Drosophila embryo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.5.6 Conclusions on the results for Drosophila embryo . . . . . . . . . . . . . 87 3.5.7 Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4 Explainable Multitarget Image Regression 99 4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.3.1 LIME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 4.3.2 Superpixel algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 4.3.3 LIME for 3D image classification . . . . . . . . . . . . . . . . . . . . . . 104 4.3.4 Image-Reg-LIME: LIME for 3D image regression . . . . . . . . . . . . . 107 4.4 Results: Classification of Aberrations . . . . . . . . . . . . . . . . . . . . . . . . 109 viii TABLE OF CONTENTS 4.4.1 Transforming the regression task into classification . . . . . . . . . . . . 110 4.4.2 Data augmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.4.3 Parameter search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 4.4.4 Clustering of 3D images . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 4.4.5 Explanations of classification . . . . . . . . . . . . . . . . . . . . . . . . 114 4.4.6 Conclusions on the results for classification . . . . . . . . . . . . . . . . 117 4.5 Results: Explainable Regression of Aberrations . . . . . . . . . . . . . . . . . . 118 4.5.1 Explanations with a reference value . . . . . . . . . . . . . . . . . . . . 121 4.5.2 Validation of explanations . . . . . . . . . . . . . . . . . . . . . . . . . . 122 4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 5 Conclusions and Outlook 127 References 129

No description available.

This thesis investigates the task of estimating the pose of a hand interacting with an object from a depth image. The main contribution of this thesis is the development of our discriminative one-shot hand-object pose estimation system. To the best of our knowledge, this is the first attempt at a one-shot hand-object pose estimation system. It is a two stage system consisting of convolutional neural networks. The first stage segments the object out of the hand from the depth image. This hand-minus-object depth image is combined with the original input depth image to form a 2-channel image for use in the second stage, pose estimation. We show that using this 2-channel image produces better pose estimation performance than a single stage pose estimation system taking just the input depth map as input. We also believe that we are amongst the first to research hand-object segmentation. We use fully convolutional neural networks to perform hand-object segmentation from a depth image. We show that this is a superior approach to random decision forests for this task. Datasets were created to train our hand-object pose estimator stage and hand-object segmentation stage. The hand-object pose labels were estimated semi-automatically with a combined manual annotation and generative approach. The segmentation labels were inferred automatically with colour thresholding. To the best of our knowledge, there were no public datasets for these two tasks when we were developing our system. These datasets have been or are in the process of being publicly released.

Being one of the most epoch making invention of the 21st century, computers have fundamentally altered every aspect of people&rsquo / s lives, including the education domain. Thus, for decades, computer-assisted language learning has received considerable amount of attention among researchers and language teachers by serving interactive, multi-sensory, and autonomous learning opportunities. In this respect, innumerable large or small-scale projects blazed a trail for other teachers to follow. The present study, therefore, is primarily concerned with the students&rsquo / attitudes towards computer-assisted language learning. Its main purpose is to investigate what the students&rsquo / attitudes are towards computer- assisted language learning (CALL) by also taking their attitude towards computer assisted learning (CAL) and foreign language learning (FLL) into consideration. Finally, factors affecting students&rsquo / attitudes and the relationships among computer assisted learning, computer assisted language learning and foreign language learning are also explored within the scope of the study. The findings demonstrate that most of the students have positive attitudes towards computer assisted learning, computer assisted language learning and foreign language learning. Age, grade, gender, years of studying English and prior CALL experience affect students&rsquo / attitudes. Moreover, students attitudes towards computer assisted language learning, computer- assisted language learning, and foreign language learning are, indeed, interrelated.

Tarptautiniai tyrimai rodo, jog moderniosios ugdymo priemonės gali pagerinti mokymą ir mokymąsi. Analitiniu būdu nustatyta, kad, mokant fizikos, mokomosios kompiuterinės priemonės (toliau MKP) naudojamos rečiausiai, nes daugelis programų yra užsienio kalba, jos nesuderintos su ugdymo programa ir nepritaikytos pamokoms, pernelyg sudėtingos. Atsižvelgiant į MKP analizės rezultatus, sukurta interkatyvi su bendrosiomis programomis ir išsilavinimo standartais suderinta mokomoji fizikos svetainę 8-ai klasei „Tiesiaeigis tolyginis judėjimas“. Projektuojant mokomąją svetainę, pirmenybė suteikta aktyviems ir mišriems interaktyvumo tipams bei labai aukštam, aukštam ir vidutiniam interaktyvumo lygiui. Išanalizavus tiesiaeigio tolyginio judėjimo teorinę ir praktinę dalį, parinkti mokomosios medžiagos pateikimo svetainėje būdai. Išanalizavus svetainių kūrimo priemones, parinkta Front Page 2003 programa. Svetainės interaktyvumas programuotas su Java Script kalba. Iš kelių animacijos kurimo priemonių parinkta 2D & 3D Animator programa. Su svetaine ir jos galimybėmis buvo supažindinti 8-os klasės moksleiviai. Moksleiviai teigiamai įvertino mokomąją svetainę. Svetainės mokomosios medžiagos dalys, kurių interaktyvumo lygis aukščiausias, gavo aukščiausius įvertinimus. Todėl interaktyvi mokomoji fizikos svetainė 8-ai klasei „Tiesiaeigis tolyginis judėjimas“ gali būti naudojama kaip mokymo ir mokymosi priemonė. / International researchers demonstrate that modern training aids may improve teaching and learning process. It is appointed in analytic way that interactive learning computer-aided software tools are used more rarely than in other subjects. There are some reasons for that: programess are too complicated, they are not adjusted to curriculum and most of them is in foreign language. Analysis of internet sites on physics subject indicates that they mostly are accessible easily and have suitable, comprehensible navigation, also they are quite well animated. Considering results of analysis it is decided to create an interactive internet site which is coordinated with common curriculum and with standarts of education and would be devoted for training physics subject in 8 classes. This internet site was named “Rectilineal gradual moving”. When this training internet site was being designed, preference was given to active and mixed active types as well as to very high, high and medium high level of interaction since active learning computer-aided software tools interaction type directly stimulates actions of students. When the part of theory of rectilineal gradual moving was analysed, ways of presentation training material were chosen. The site was designed with programme Front Page 2003. For creation of interaction there are explored two language in script writing: VBScript and Java Script. Considering that these languages have not essential differences interaction of learning site... [to full text]

Orientador: Sérgio Ferreira do Amaral / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Educação / Made available in DSpace on 2018-08-25T19:14:27Z (GMT). No. of bitstreams: 1 Araujo_IzabelCristinade_D.pdf: 4104644 bytes, checksum: b2e270b8879cdf763e6800edb77559f1 (MD5) Previous issue date: 2013 / Resumo: Essa pesquisa tem como objetivo geral desenvolver uma proposta didático-pedagógica para ambiente virtual de aprendizagem assistida por computador (AAC). Os objetivos específicos situam-se em: identificar referenciais didático-pedagógicos junto à literatura e especialistas da área, construir um quadro indicativo dos referenciais didático-pedagógicos de ambiente virtual de AAC e sistematizar os referenciais encontrados, agrupando-os em unidades de análise para composição das diretrizes norteadoras do desenvolvimento da proposta didático-pedagógica. O problema da pesquisa apresentou-se em: Como referenciais didático-pedagógicos podem nortear ações educativas em ambientes virtuais de AAC? Realizamos trabalho de campo com levantamento e revisão da literatura, anotações em diário de campo advindas da observação em campo e entrevista semi-estruturada. Tivemos a participação de 36 pesquisadores de diferentes universidades americanas, asiáticas, europeias e da Oceania. A análise dos dados de predominância qualitativa norteou as conclusões, quais sejam: que o investimento em pesquisa na área de educação com inovação tecnológica proporciona resultados práticos, impactando na formulação de políticas públicas e na formação de professores; a relevância da autoria do professor na ação educativa em ambiente virtual de AAC; o professor-autor como mediador da aprendizagem; as destrezas e os conhecimentos necessários para utilizar os materiais de ambiente de AAC se apresentam mais eficazes se autores e coautores contarem com formação básica para a utilização das ferramentas tecnológicas. Daí a importância de fazê-lo gradualmente para que sejam capazes de aumentar seu nível de autonomia frente a sua própria aprendizagem.Essa pesquisa tem como objetivo geral desenvolver uma proposta didático-pedagógica para ambiente virtual de aprendizagem assistida por computador (AAC). Os objetivos específicos situam-se em: identificar referenciais didático-pedagógicos junto à literatura e especialistas da área, construir um quadro indicativo dos referenciais didático-pedagógicos de ambiente virtual de AAC e sistematizar os referenciais encontrados, agrupando-os em unidades de análise para composição das diretrizes norteadoras do desenvolvimento da proposta didático-pedagógica. O problema da pesquisa apresentou-se em: Como referenciais didático-pedagógicos podem nortear ações educativas em ambientes virtuais de AAC? Realizamos trabalho de campo com levantamento e revisão da literatura, anotações em diário de campo advindas da observação em campo e entrevista semi-estruturada. Tivemos a participação de 36 pesquisadores de diferentes universidades americanas, asiáticas, europeias e da Oceania. A análise dos dados de predominância qualitativa norteou as conclusões, quais sejam: que o investimento em pesquisa na área de educação com inovação tecnológica proporciona resultados práticos, impactando na formulação de políticas públicas e na formação de professores; a relevância da autoria do professor na ação educativa em ambiente virtual de AAC; o professor-autor como mediador da aprendizagem; as destrezas e os conhecimentos necessários para utilizar os materiais de ambiente de AAC se apresentam mais eficazes se autores e coautores contarem com formação básica para a utilização das ferramentas tecnológicas. Daí a importância de fazê-lo gradualmente para que sejam capazes de aumentar seu nível de autonomia frente a sua própria aprendizagem / Abstract: The overall goal of this research work is to develop a didactic proposal for Computer Assisted Learning (CAL) environments. The specific goals are: to identify the didactic requirements from both the literature and the specialists in the field; to build a conceptual framework of the didactic requirements of a virtual CAL environment; and to organize the requirements found by creating categories based on units of analysis so as to build up the main requirements of the development of a teaching proposal. The main concern was: How can pedagogical criteria set the foundations for sound educational actions in computer-assisted learning environments? 36 researchers from different universities in America, Asia, Europe and Australia participated in the research. The data analysis led to the following conclusions: a) the investment in research in the field of technology-enhanced teaching and learning leads to practical results which in turn foster the emergence of public policies and the improvement of teacher training; b) the relevance of teacher authorship in education in virtual CAL environments should be pointed out, the starting point being the social and cultural environment of the school where the teaching-learning process takes place; c) the role of the teacher-author as a mediator in learning should be borne in mind; d) the skills and knowledge needed when using the materials of a CAL environment become more effective when used by authors and co-authors trained in the use of technological tools. This means that it is important to provide authors and co-authors with the opportunity to develop those competences gradually so as to increase their level of autonomy regarding their own learning process / Doutorado / Ciencias Sociais na Educação / Doutora em Educação