Collaborative technologies, higher order thinking and self-sufficient learning: A case study of adult learners

The inclusion of online elements in learning environments is becoming commonplace in Post Compulsory Education. A variety of research into the value of such elements is available, and this study aims to add further evidence by looking specifically at the use of collaborative technologies such as online discussion forums and wikis to encourage higher order thinking and self-sufficient learning. In particular, the research examines existing pedagogical models including Salmon’s five-stage model, along with other relevant literature. A case study of adult learners in community-based learning centres forms the basis of the research, and as a result of the findings, an arrow model is suggested as a framework for online collaboration that emphasises the learner, mentions pre-course preparation and then includes three main phases of activity: post, interact and critique. This builds on Salmon’s five-stage model and has the benefit of being flexible and responsive, as well as allowing for further development beyond the model, particularly in a blended learning environment.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Inside standard and hyperspectral low-dose CT variational imaging: parameter identification and material decomposition

The main contribution of this thesis is the proposal of novel strategies for the selection of parameters arising in variational models employed for the solution of inverse problems with data corrupted by Poisson noise. In light of the importance of using a significantly small dose of X-rays in Computed Tomography (CT), and its need of using advanced techniques to reconstruct the objects due to the high level of noise in the data, we will focus on parameter selection principles especially for low photon-counts, i.e. low dose Computed Tomography. For completeness, since such strategies can be adopted for various scenarios where the noise in the data typically follows a Poisson distribution, we will show their performance for other applications such as photography, astronomical and microscopy imaging. More specifically, in the first part of the thesis we will focus on low dose CT data corrupted only by Poisson noise by extending automatic selection strategies designed for Gaussian noise and improving the few existing ones for Poisson. The new approaches will show to outperform the state-of-the-art competitors especially in the low-counting regime. Moreover, we will propose to extend the best performing strategy to the hard task of multi-parameter selection showing promising results. Finally, in the last part of the thesis, we will introduce the problem of material decomposition for hyperspectral CT, which data encodes information of how different materials in the target attenuate X-rays in different ways according to the specific energy. We will conduct a preliminary comparative study to obtain accurate material decomposition starting from few noisy projection data.

Variational Quantum Splines: Moving Beyond Linearity for Quantum Activation Functions

Activation functions within neural networks play a crucial role in Deep Learning since they allow to learn complex and non-trivial patterns in the data. However, the ability to approximate non-linear functions is a significant limitation when implementing neural networks in a quantum computer to solve typical machine learning tasks. The main burden lies in the unitarity constraint of quantum operators, which forbids non-linearity and poses a considerable obstacle to developing such non-linear functions in a quantum setting. Nevertheless, several attempts have been made to tackle the realization of the quantum activation function in the literature. Recently, the idea of the QSplines has been proposed to approximate a non-linear activation function by implementing the quantum version of the spline functions. Yet, QSplines suffers from various drawbacks. Firstly, the final function estimation requires a post-processing step; thus, the value of the activation function is not available directly as a quantum state. Secondly, QSplines need many error-corrected qubits and a very long quantum circuits to be executed. These constraints do not allow the adoption of the QSplines on near-term quantum devices and limit their generalization capabilities. This thesis aims to overcome these limitations by leveraging hybrid quantum-classical computation. In particular, a few different methods for Variational Quantum Splines are proposed and implemented, to pave the way for the development of complete quantum activation functions and unlock the full potential of quantum neural networks in the field of quantum machine learning.

Prosperity Thinking in Action: a case study from Future Food Institute’s Paideia Campus

The need for sustainable economic growth and environmental stewardship emerged around the start of the twentieth century when society became aware that the traditional development model would lead to the collapse of the terrestrial ecosystem in the long run. Over the years, the international community's environmental efforts have demonstrated unequivocally that the planet's limits are real. And so, the new development approach has laid the groundwork for the future. According to this model, design also plays a key role in ensuring a better future. The design has undergone an ecological and sustainable evolution as a result of the global environmental crisis and the degradation of our ecosystem and biodiversity. In this contest, Prosperity Thinking is inserted, a still evolving methodology developed by the Future Food Institute starting from 2019. The main concepts on which it is based are described, as well as the method that identifies it, which is divided into the following stages: 1) Problem Framing 2) Ideation and Prototyping 3) Test & Analyze. The development of the prosperity thinking toolkit is described, beginning with the search for tools from the literature on sustainable design and ending with its validation with the help of design experts. The testing of some tools will be recounted during a workshop organized by FFI, in which 15 people ranging in age from 14 to 40 will participate, and then the final version of the toolkit will be presented which has been obtained by adding to it the tools proposed by the experts. Finally, a reflection on the future of Prosperity Thinking, a method in constant evolution that must continue to follow societal and environmental changes in order to respond to the ever-increasingly complex challenge of sustainability.

Integrare gli approcci all'innovazione Data Science e Design Thinking: analisi teorica e applicazione al caso della sanità parmense

Nel panorama aziendale odierno, risulta essere di fondamentale importanza la capacità, da parte di un’azienda o di una società di servizi, di orientare in modo programmatico la propria innovazione in modo tale da poter essere competitivi sul mercato. In molti casi, questo e significa investire una cospicua somma di denaro in progetti che andranno a migliorare aspetti essenziali del prodotto o del servizio e che avranno un importante impatto sulla trasformazione digitale dell’azienda. Lo studio che viene proposto riguarda in particolar modo due approcci che sono tipicamente in antitesi tra loro proprio per il fatto che si basano su due tipologie di dati differenti, i Big Data e i Thick Data. I due approcci sono rispettivamente il Data Science e il Design Thinking. Nel corso dei seguenti capitoli, dopo aver definito gli approcci di Design Thinking e Data Science, verrà definito il concetto di blending e la problematica che ruota attorno all’intersezione dei due metodi di innovazione. Per mettere in evidenza i diversi aspetti che riguardano la tematica, verranno riportati anche casi di aziende che hanno integrato i due approcci nei loro processi di innovazione, ottenendo importanti risultati. In particolar modo verrà riportato il lavoro di ricerca svolto dall’autore riguardo l'esame, la classificazione e l'analisi della letteratura esistente all'intersezione dell'innovazione guidata dai dati e dal pensiero progettuale. Infine viene riportato un caso aziendale che è stato condotto presso la realtà ospedaliero-sanitaria di Parma in cui, a fronte di una problematica relativa al rapporto tra clinici dell’ospedale e clinici del territorio, si è progettato un sistema innovativo attraverso l’utilizzo del Design Thinking. Inoltre, si cercherà di sviluppare un’analisi critica di tipo “what-if” al fine di elaborare un possibile scenario di integrazione di metodi o tecniche provenienti anche dal mondo del Data Science e applicarlo al caso studio in oggetto.

Non-gaussian Spatial Correlations Dramatically Weaken Localization.

We perform variational studies of the interaction-localization problem to describe the interaction-induced renormalizations of the effective (screened) random potential seen by quasiparticles. Here we present results of careful finite-size scaling studies for the conductance of disordered Hubbard chains at half-filling and zero temperature. While our results indicate that quasiparticle wave functions remain exponentially localized even in the presence of moderate to strong repulsive interactions, we show that interactions produce a strong decrease of the characteristic conductance scale g^{*} signaling the crossover to strong localization. This effect, which cannot be captured by a simple renormalization of the disorder strength, instead reflects a peculiar non-Gaussian form of the spatial correlations of the screened disordered potential, a hitherto neglected mechanism to dramatically reduce the impact of Anderson localization (interference) effects.

Resolvendo a equação de Schrödinger utilizando procedimentos numéricos fundamentais

A combination of the variational principle, expectation value and Quantum Monte Carlo method is used to solve the Schrödinger equation for some simple systems. The results are accurate and the simplicity of this version of the Variational Quantum Monte Carlo method provides a powerful tool to teach alternative procedures and fundamental concepts in quantum chemistry courses. Some numerical procedures are described in order to control accuracy and computational efficiency. The method was applied to the ground state energies and a first attempt to obtain excited states is described.

Modelos organizadores do pensamento: uma perspectiva de pesquisa sobre o raciocínio moral com adolescentes autores de infração

This article has the aim to expand the perspective of research in the field of morality. We present a proposal of morality study of outlaw teenagers according to Thinking Organizer Models Theory. Through the idea of complexity we search to understand the cognitive process in the elaboration of moral reasoning inside situations of conflict. With this perspective, we developed a research that aimed to identify which organizer models were applied by 20 outlaw male teenagers who abide by social punishment to solve the hypothetical moral conflicts. Through interviews we told them a situation of moral conflict that involved friendship relation, physical aggression and steal. We could identified several models which were joined in three categories. Such models reflected the diversity and regularity that are present inside the elaborated reasoning to solve the conflicts shown by us. We conclude that the diversity of organizer models identified shows the importance of the contents in the construction of moral reasoning.

Escola, democracia e a construção de personalidades morais

This article intends to discuss the relationship between morality, democracy and education within the perspective of the complex thinking, pointing to paths and proposals for its effective implementation in the educational routine, under the conviction that this is an imperative of the new social demands presented to the contemporary schooling. Understanding that one of the purposes of education is the ethical development, the author proposes intentional actions such that through them the school practices can offer to the subjects of education the necessary tools to build their cognitive, affective, cultural, and organic competence, thereby enabling them to act morally in the world. To that effect, seven aspects of school reality that hamper or contribute to school democratization are identified and discussed, which must be understood from the paradigm of complexity: school contents, classroom methodology, the nature of interpersonal relationships, the values, self-esteem and self-knowledge of the school community, as well as the school management processes.

Autoscopia: um procedimento de pesquisa e de formação

This article considers a procedure for data collection called autoscopy. Autoscopy entails the video recording of a practice with the purpose of allowing analysis and self-evaluation by one of the protagonists of that practice. The objective of the video recording is that of apprehending the actions of the agent (or agents), the scenario, and the plot that make up a situation. The recorded material is subjected to sessions of analysis after the action that aim at the understanding of the reflective process of the agent (or agents) through their verbalizations during the analysis of video recorded scenes. The present text introduces a theoretical basis for the procedure of autoscopy, deals with advantages and limitations of its use, as well as with aspects that deserve attention and, finally, describes the authors' experiences in two studies in which the procedure was employed. Starting from these two experiences, differences and similarities are pointed out between the studies, especially regarding the participants, object, and the time distribution of the video recordings. The authors draw considerations about the formative-reflective potential of the procedure, both for research situations and for the learning and training of various professionals, considering it to be an excellent educational instrument. It is, however, vital to keep in mind the need to recognize and return to the teacher, as an autoscopic participant, his condition as subject of his own profession, thereby promoting, besides the self-evaluation, also the autonomy of his thinking and doing.

A educação física na educação infantil e as relações de gênero : educando crianças ou meninos e meninas?

Universidade Estadual de Campinas . Faculdade de Educação Física

Esgrima em cadeira de rodas : pedagogia de ensino a partir das dimensões e contexto da modalidade

Universidade Estadual de Campinas . Faculdade de Educação Física

Procedimentos pedagogicos para o ensino das lutas : contextos e possibilidades

Universidade Estadual de Campinas . Faculdade de Educação Física

Dança... ensino, sentidos e possibilidades na escola

Universidade Estadual de Campinas. Faculdade de Educação Física