Self-regulated learning in higher education : strategies adopted by computer programming students

Trabalho apresentado em PAEE/ALE’2016, 8th International Symposium on Project Approaches in Engineering Education (PAEE) and 14th Active Learning in Engineering Education Workshop (ALE)

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

Socratic flipped classroom : What types of questions and tasks promote learning?

Socratic questioning stresses the importance of questioning for learning. Flipped Classroom pedagogy generates a need for effective questions and tasks in order to promote active learning. This paper describes a project aimed at finding out how different kinds of questions and tasks support students’ learning in a flipped classroom context. In this study, during the flipped courses, both the questions and tasks were distributed together with video recordings. Answers and solutions were presented and discussed in seminars, with approximately 10 participating students in each seminar. Information Systems students from three flipped classroom courses at three different levels were interviewed in focus groups about their perceptions of how different kinds of questions and tasks supported their learning process. The selected courses were organized differently, with various kinds of questions and tasks. Course one included open questions that were answered and presented at the seminar. Students also solved a task and presented the solution to the group. Course two included open questions and a task. Answers and solutions were discussed at the seminars where students also reviewed each other’s answers and solutions. Course three included online single- and multiple choice questions with real-time feedback. Answers were discussed at the seminar, with the focus on any misconceptions. In this paper we categorized the questions in accordance with Wilson (2016) as factual, convergent, divergent, evaluative, or a combination of these. In all, we found that any comprehensible question that initiates a dialogue, preferably with a set of Socratic questions, is perceived as promoting learning. This is why seminars that allow such questions and discussion are effective. We found no differences between the different kinds of Socratic questions. They were seen to promote learning so long as they made students reflect and problematize the questions. To conclude, we found that questions and tasks promote learning when they are answered and solved in a process that is characterized by comprehensibility, variation, repetition and activity.

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Robotic Manipulation of DLOs for Wiring Harness Assembly: a Machine Learning Approach

In recent times, a significant research effort has been focused on how deformable linear objects (DLOs) can be manipulated for real world applications such as assembly of wiring harnesses for the automotive and aerospace sector. This represents an open topic because of the difficulties in modelling accurately the behaviour of these objects and simulate a task involving their manipulation, considering a variety of different scenarios. These problems have led to the development of data-driven techniques in which machine learning techniques are exploited to obtain reliable solutions. However, this approach makes the solution difficult to be extended, since the learning must be replicated almost from scratch as the scenario changes. It follows that some model-based methodology must be introduced to generalize the results and reduce the training effort accordingly. The objective of this thesis is to develop a solution for the DLOs manipulation to assemble a wiring harness for the automotive sector based on adaptation of a base trajectory set by means of reinforcement learning methods. The idea is to create a trajectory planning software capable of solving the proposed task, reducing where possible the learning time, which is done in real time, but at the same time presenting suitable performance and reliability. The solution has been implemented on a collaborative 7-DOFs Panda robot at the Laboratory of Automation and Robotics of the University of Bologna. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, but showing at the same time a high success rate from the very beginning of the learning phase.

Deep learning based style transfer for low altitude aerial imagery

Unmanned Aerial Vehicle (UAVs) equipped with cameras have been fast deployed to a wide range of applications, such as smart cities, agriculture or search and rescue applications. Even though UAV datasets exist, the amount of open and quality UAV datasets is limited. So far, we want to overcome this lack of high quality annotation data by developing a simulation framework for a parametric generation of synthetic data. The framework accepts input via a serializable format. The input specifies which environment preset is used, the objects to be placed in the environment along with their position and orientation as well as additional information such as object color and size. The result is an environment that is able to produce UAV typical data: RGB image from the UAVs camera, altitude, roll, pitch and yawn of the UAV. Beyond the image generation process, we improve the resulting image data photorealism by using Synthetic-To-Real transfer learning methods. Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different - although related - problem. This approach has been widely researched in other affine fields and results demonstrate it to be an interesing area to investigate. Since simulated images are easy to create and synthetic-to-real translation has shown good quality results, we are able to generate pseudo-realistic images. Furthermore, object labels are inherently given, so we are capable of extending the already existing UAV datasets with realistic quality images and high resolution meta-data. During the development of this thesis we have been able to produce a result of 68.4% on UAVid. This can be considered a new state-of-art result on this dataset.

Classificação das tendencias da educação fisica : uma abordagem filosofica-educacional e ideologica

Universidade Estadual de Campinas . Faculdade de Educação Física

Artigos científicos como recurso didático no ensino superior de Química

Chemistry teachers increasingly use research articles in their undergraduate courses. This trend arises from current pedagogical emphasis on active learning and scientific process. In this paper, we describe some educational experiences on the use of research articles in chemistry higher education. Additionally, we present our own conclusions on the use of such methodology applied to a scientific communication course offered to undergraduate chemistry students at the University of São Paulo, Brazil.

Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method`s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright (C) 2009 John Wiley & Sons, Ltd.

Biochemistry of the Envenomation Response-a Generator Theme for Interdisciplinary Integration

The understanding of complex physiological processes requires information from many different areas of knowledge. To meet this interdisciplinary scenario, the ability of integrating and articulating information is demanded. The difficulty of such approach arises because, more often than not, information is fragmented through under graduation education in Health Sciences. Shifting from a fragmentary and deep view of many topics to joining them horizontally in a global view is not a trivial task for teachers to implement. To attain that objective we proposed a course herein described Biochemistry of the envenomation response aimed at integrating previous contents of Health Sciences courses, following international recommendations of interdisciplinary model. The contents were organized by modules with increasing topic complexity. The full understanding of the envenoming pathophysiology of each module would be attained by the integration of knowledge from different disciplines. Active-learning strategy was employed focusing concept map drawing. Evaluation was obtained by a 30-item Likert-type survey answered by ninety students; 84% of the students considered that the number of relations that they were able to establish as seen by concept maps increased throughout the course. Similarly, 98% considered that both the theme and the strategy adopted in the course contributed to develop an interdisciplinary view.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Pulmonary involvement in pleural tuberculosis: How often does it mean disease activity?

Objective: To evaluate in chest X-rays and high-resolution computed tomographies of patients with pleural tuberculosis, the incidence of parenchymal and mediastinal lung lesions suggestive of active disease. Methods: Prospective study (2008-2009) evaluating the radiographic and tomographic abnormalities of 88 HIV-negative patients with pleural tuberculosis (unilateral effusion). The images were reviewed by 3 independent specialists, and the observed changes were classified according to previously established criteria: presence or absence of signs suggestive of disease activity, and nonspecific findings. Results: Abnormal changes were observed in chest X-rays of 22 (25%) patients and in the computed tomography of 55 (63%). Images compatible with active pulmonary tuberculosis were detected by radiography in 9 (10%) patients and by tomography in 38 (43%). Only 4 (4.5%) patients had tomography images suggestive of residual disease. Conclusion: The present study demonstrates that pulmonary involvement is quite common in pleural tuberculosis. This finding is mainly observed in high-resolution computed tomography and has important epidemiological implications, since patients with pleural tuberculosis are significant sources of infection and disease dissemination. (C) 2011 Elsevier Ltd. All rights reserved.

Plano de implementa????o do projeto de desenvolvimento de capacidade de governan??a

Desde a d??cada de 1990, o Governo Federal brasileiro vem implementando uma agenda ambiciosa de reformas do Estado, centradas na redu????o da pobreza e na melhoria da efici??ncia dos servi??os p??blicos. As principais prioridades, conforme previstas no Plano Plurianual (PPA) para o per??odo 2003-2007, s??o as seguintes: inclus??o social e redu????o da desigualdade; crescimento econ??mico com gera????o de emprego; distribui????o de renda e respeito ao meio ambiente; promo????o e amplia????o dos direitos de cidadania; e fortalecimento da democracia. No in??cio de 2006, o Governo criou a Pol??tica Nacional de Desenvolvimento de Pessoal (Decreto 5.707), com o objetivo de melhorar e aumentar a efici??ncia e a efic??cia na presta????o de servi??os p??blicos. No marco dessa pol??tica recente, as escolas de administra????o p??blica desempenham um papel fundamental na identifica????o das compet??ncias que precisam ser desenvolvidas nas institui????es do governo, bem como na implementa????o de pol??ticas de capacita????o para os servidores p??blicos, diretamente e/ou em parceria com escolas de governo nos n??veis federal, estadual ou local. O Canad?? tamb??m est?? criando uma estrutura para levantar as compet??ncias necess??rias para os servidores p??blicos e desenvolv??-las como um componente da Renova????o do Servi??o P??blico em todo o governo. Como institui????es l??deres no desenvolvimento de compet??ncias de servidores p??blicos, a Canada School of Public Service (CSPS) e a Escola Nacional de Administra????o P??blica (ENAP) firmaram uma parceria para implementar o Projeto de Desenvolvimento de Capacidade de Governan??a no Brasil. A finalidade do Projeto ?? melhorar a capacidade de servidores p??blicos federais, estaduais e municipais do Brasil para desenvolver e implementar programas de capacita????o e gerenciar pol??ticas p??blicas descentralizadas. Espera-se que essa parceria e o resultante compartilhamento de experi??ncias em capacita????o para governan??a efetiva contribuam para a redu????o da pobreza e das desigualdades no Brasil, por meio do desenvolvimento de compet??ncias de servidores na presta????o de servi??os p??blicos eficazes e eficientes, voltados para o cidad??o. O Projeto re??ne, al??m das duas principais Escolas de Governo no Canad?? e no Brasil, seis Escolas Brasileiras de Administra????o P??blica regionais e duas renomadas Institui????es Acad??micas Canadenses ??? a Queen???s University e a Western Ontario University. O Minist??rio do Desenvolvimento Social e Combate ?? Fome (MDS) e tr??s Secretarias Especiais do Governo Federal ??? Ra??a (SEPPIR), Direitos Humanos (SEDH) e Pol??ticas para as Mulheres (SPM) ??? tamb??m se envolver??o nas atividades de compartilhamento de conhecimentos com o Human Resources and Skills Development Canada (HRSDC) e a Canada Public Service Agency (CPSA). A CIDA fornecer?? CND$1.700.000 por meio do Programa Brasil-Canad?? de Interc??mbio de Conhecimentos para a Promo????o da Equidade (PIPE). A contribui????o da ENAP ser?? de CND$1.069.707 em esp??cie. A CSPS contribuir?? com cursos, al??m de conhecimentos e suporte t??cnicos, avaliados em CND$1.000.000. Aproveitando a parceria entre a CSPS e a ENAP, que resultou na transfer??ncia e na adapta????o bem sucedidas de cursos e metodologias canadenses, o novo projeto extrapola o n??cleo do servi??o p??blico em Bras??lia, alcan??ando escolas de governo em regi??es brasileiras em situa????o de desvantagem. ?? semelhan??a do papel da CSPS no primeiro projeto, a ENAP fortalecer?? a capacidade das escolas parceiras regionais para capacitar servidores p??blicos envolvidos na presta????o de servi??os aos brasileiros. O interc??mbio estruturado entre Minist??rios dos Governos canadense e brasileiro tamb??m aplicar?? a aprendizagem mais diretamente a quest??es de pol??ticas e programas sociais do Brasil. O desafio assumido neste Projeto ?? a adapta????o de conhecimentos e aprendizagem, com vistas a melhorar a implementa????o de pol??ticas e programas sociais. Para tanto, a CSPS e a ENAP introduzir??o novos cursos nos curr??culos das escolas parceiras e incorporar??o novos m??todos e tecnologias de aprendizagem como, por exemplo, comunidades de pr??tica virtuais e um componente de tutoria (mentoring) envolvendo o Human Resources and Skills Development Canada e o Minist??rio do Desenvolvimento Social e Combate ?? Fome do Brasil. Seis institui????es da Rede Nacional de Escolas de Governo do Brasil e do Programa de Parceria da ENAP foram selecionadas e convidadas a se unir ?? CSPS e ?? ENAP nesse novo Projeto: a Universidade Federal do Par?? (UFPA), de Bel??m (estado do Par?? ??? regi??o Norte); a Funda????o Joaquim Nabuco (FUNDAJ), de Recife (Pernambuco ??? Nordeste); a Universidade Corporativa do Servi??o P??blico / Secretaria de Administra????o do Estado da Bahia (UCS/SAEB), Salvador (Bahia ??? Nordeste); a Escola de Governo do Mato Grosso do Sul (ESCOLAGOV), Campo Grande (estado do Mato Grosso do Sul ??? Centro-Oeste); a Escola Nacional de Ci??ncias Estat??sticas / Instituto Brasileiro de Geografia e Estat??stica (ENCE/IBGE), Rio de Janeiro (estado do Rio de Janeiro ??? Sudeste); e o Instituto Municipal de Administra????o P??blica (IMAP) de Curitiba (Paran?? ??? Sul). Essas escolas de refer??ncia foram escolhidas segundo sua capacidade de trabalhar como p??los de pr??ticas inovadoras em pol??ticas p??blicas e disseminar os benef??cios do Projeto para outras escolas em suas regi??es, por meio da Rede Nacional coordenada pela ENAP. O objetivo dessa parceria ?? fortalecer as escolas de governo locais, para que estas desenvolvam, por meio de eventos de aprendizagem, compet??ncias em servidores p??blicos, a fim de aumentar a capacidade do governo na implementa????o e gest??o de pol??ticas p??blicas. O Plano de Implementa????o do Projeto (PIP) descreve o trabalho a ser realizado por essas institui????es nos pr??ximos 30 meses, ao tempo em que serve de guia para os Parceiros do Projeto no que se refere ??s a????es e aos recursos necess??rios para a obten????o dos resultados acordados. Na medida em que o Projeto estiver em andamento e os parceiros iniciarem um interc??mbio produtivo de conhecimentos, o Plano de Trabalho Anual ser?? atualizado e revisto por meio de reuni??es anuais de avalia????o e encontros do Comit?? Diretor do Projeto, com vistas a assegurar que os resultados descritos no PIP sejam alcan??ados com sucesso

Aprendizagem da leitura em crianças com dificuldade intelectual e desenvolvimental

A presente investigação tem como objecto a aprendizagem e as práticas de leitura por parte de crianças com Dificuldade Intelectual e Desenvolvimental (DID). Na revisão bibliográfica efectuada constata-se a existência de uma ligação intrínseca entre os processos cognitivos e a aprendizagem da leitura, sendo estes um factor determinante para a compreensão do processo de ler. Tivemos a preocupação de dar a conhecer os componentes implicados na leitura, nomeadamente a descodificação e a compreensão, a primeira das quais integra os processos cognitivos de âmbito fonológico. Salienta-se a interligação de ambas as componentes para a aquisição da técnica da leitura e os modelos e métodos de ensino da leitura que vão condicionar a sua aprendizagem. Procurou-se perceber quais as causas da dificuldade na aprendizagem da leitura em crianças com DID, bem como a influência de diferentes métodos na aprendizagem da leitura. Três objectivos/preocupações nortearam a investigação: a) identificar quais os processos cognitivos implicados nas dificuldades da leitura em crianças com DID e sem DID; b) identificar os processos fonológicos envolvidos na aprendizagem da leitura em crianças com DID e sem DID; c) identificar se o método de ensino que o professor utiliza para ensinar a ler tem influência na aprendizagem de crianças com DID e sem DID. Para responder à questão de partida procedeu-se a um estudo quase experimental e comparativo. A dimensão empírica desta investigação assentou numa amostra constituída por doze alunos e seis professores de duas escolas do ensino público. Dos alunos, 8 não tinham DID (4 do 1º ano de escolaridade e 4 do 3º ano de escolaridade), e 4 tinham DID (2 do 4º ano de escolaridade e 2 do 5º e o 7º ano de escolaridade). Aos dois alunos com DID do 4º ano de escolaridade, foi aplicada uma intervenção pedagógica de reforço da aprendizagem da leitura durante 14 sessões. Os restantes alunos constituíram o grupo de controlo. Os resultados obtidos a partir da aplicação da bateria de testes, antes e após a intervenção, permite-nos percepcionar que as dificuldades na aprendizagem da leitura possivelmente, estarão mais relacionadas com os processos fonológicos e não tanto com os processos cognitivos.