El aprendizaje basado en problemas (ABP) para la enseñanza de las ciencias naturales en quinto año de educación general básica

La presente investigación pretende valorar la importancia de enseñar Ciencias Naturales a través de la estrategia del Aprendizaje Basado en Problemas (ABP), con la finalidad de mejorar las prácticas pedagógicas de los docentes, y a partir de la implementación del ABP desarrollar en los estudiantes aprendizajes significativos. Para esta investigación se utilizó una recopilación bibliográfica y se tomó como principal punto de partida el ABP y la Actualización y Fortalecimiento Curricular del 2010 en Ciencias Naturales, con la finalidad de responder a las siguientes preguntas: ¿Es posible planificar las clases de Ciencias Naturales a partir del ABP? ¿Cuáles son los pasos a seguir para implementar el ABP en Ciencias Naturales? Los resultados muestran que es posible implementar el ABP en las clases de Ciencias Naturales porque permite construir el nuevo conocimiento sobre la base de los conocimientos previos. En Ciencias Naturales es imprescindible generar en los estudiantes actitudes de protección y cuidado hacia el medio, aspectos que se consigue mediante la solución de problemas. Finalmente, se hizo una integración de varios autores sobre los pasos que se deben seguir para la implementación del ABP en Ciencias Naturales, el mismo que se organizó en nueve pasos.

Évaluation de la plateforme de formation en ligne MOTIV@CŒUR sur les interventions motivationnelles brèves auprès d’infirmières en soins aigus cardiovasculaires

Dans un contexte de prévention secondaire, les interventions motivationnelles brèves (IMB) effectuées par les infirmières ont le potentiel de réduire les facteurs de risque cardiovasculaires. De par sa flexibilité, la formation en ligne s’impose aujourd’hui comme une méthode pédagogique essentielle au développement des habiletés cliniques des professionnels de la santé. Le but de ce projet était d’évaluer la faisabilité, l’acceptabilité et l’effet préliminaire d’une plateforme de formation en ligne sur les IMB (MOTIV@CŒUR) sur les habiletés perçues et l’utilisation clinique des IMB chez des infirmières en soins cardiovasculaires. Pour ce faire, une étude pilote pré-post à groupe unique a été menée. MOTIV@CŒUR est composée de deux sessions d’une durée totale de 50 minutes incluant des vidéos d’interactions infirmière-patient. Dans chaque session, une introduction théorique aux IMB est suivie de situations cliniques dans lesquelles une infirmière évalue la motivation à changer et intervient selon les principes des IMB. Les situations ciblent le tabagisme, la non-adhérence au traitement médicamenteux, la sédentarité et une alimentation riche en gras et en sel. Il était suggéré aux infirmières de compléter les deux sessions de formation en ligne en moins de 20 jours. Les données sur la faisabilité, l'acceptabilité et les effets préliminaires (habiletés perçues et utilisation clinique auto-rapportée des IMB) ont été recueillies à 30 jours (± 5 jours) après la première session. Nous avons recruté 27 femmes et 4 hommes (âge moyen 37 ans ± 9) en mars 2016. Vingt-quatre des 31 participants (77%) ont terminé les deux sessions de formation en moins de 20 jours. À un mois suite à l’entrée dans l’étude, 28 des 31 participants avaient complété au moins une session. Un haut niveau d’acceptabilité a été observé vu les scores élevés quant à la qualité de l'information, la facilité d'utilisation perçue et la qualité de la plateforme MOTIV@CŒUR. Le score d'utilisation clinique auto-rapporté des interventions visant la confiance était plus élevé après les deux sessions qu’avant les sessions (P = .032). Bien que tous les scores fussent plus élevés après les deux sessions qu’au début, les autres résultats n’étaient pas statistiquement significatifs. En conclusion, l’implantation d’une plateforme de formation en ligne sur les IMB est à la fois faisable et acceptable auprès d’infirmières en soins aigus cardiovasculaires. De plus, une telle formation peut avoir un effet positif sur l'utilisation clinique d’interventions motivationnelles visant la confiance face au changement de comportement de santé.

Évaluation de la plateforme de formation en ligne MOTIV@CŒUR sur les interventions motivationnelles brèves auprès d’infirmières en soins aigus cardiovasculaires

Dans un contexte de prévention secondaire, les interventions motivationnelles brèves (IMB) effectuées par les infirmières ont le potentiel de réduire les facteurs de risque cardiovasculaires. De par sa flexibilité, la formation en ligne s’impose aujourd’hui comme une méthode pédagogique essentielle au développement des habiletés cliniques des professionnels de la santé. Le but de ce projet était d’évaluer la faisabilité, l’acceptabilité et l’effet préliminaire d’une plateforme de formation en ligne sur les IMB (MOTIV@CŒUR) sur les habiletés perçues et l’utilisation clinique des IMB chez des infirmières en soins cardiovasculaires. Pour ce faire, une étude pilote pré-post à groupe unique a été menée. MOTIV@CŒUR est composée de deux sessions d’une durée totale de 50 minutes incluant des vidéos d’interactions infirmière-patient. Dans chaque session, une introduction théorique aux IMB est suivie de situations cliniques dans lesquelles une infirmière évalue la motivation à changer et intervient selon les principes des IMB. Les situations ciblent le tabagisme, la non-adhérence au traitement médicamenteux, la sédentarité et une alimentation riche en gras et en sel. Il était suggéré aux infirmières de compléter les deux sessions de formation en ligne en moins de 20 jours. Les données sur la faisabilité, l'acceptabilité et les effets préliminaires (habiletés perçues et utilisation clinique auto-rapportée des IMB) ont été recueillies à 30 jours (± 5 jours) après la première session. Nous avons recruté 27 femmes et 4 hommes (âge moyen 37 ans ± 9) en mars 2016. Vingt-quatre des 31 participants (77%) ont terminé les deux sessions de formation en moins de 20 jours. À un mois suite à l’entrée dans l’étude, 28 des 31 participants avaient complété au moins une session. Un haut niveau d’acceptabilité a été observé vu les scores élevés quant à la qualité de l'information, la facilité d'utilisation perçue et la qualité de la plateforme MOTIV@CŒUR. Le score d'utilisation clinique auto-rapporté des interventions visant la confiance était plus élevé après les deux sessions qu’avant les sessions (P = .032). Bien que tous les scores fussent plus élevés après les deux sessions qu’au début, les autres résultats n’étaient pas statistiquement significatifs. En conclusion, l’implantation d’une plateforme de formation en ligne sur les IMB est à la fois faisable et acceptable auprès d’infirmières en soins aigus cardiovasculaires. De plus, une telle formation peut avoir un effet positif sur l'utilisation clinique d’interventions motivationnelles visant la confiance face au changement de comportement de santé.

Clase Invertida

En este segundo número de reflexiones pedagógicas se presenta una revisión de la denominada clase invertida (flipped classroom). Este texto presentan los componentes que caracterizan esta estrategia. Se comparan igualmente los elementos que la diferencian de la clase tradicional y se destacan los pasos para adelantar esta innovación y su forma de funcionamiento. De igual manera se muestran algunos indicadores que pueden llevar a una reflexión de la pacífica pedagógica y se concluye con estudios que muestran sus aportes e investigaciones que la soportan.

Efectos de una intervención educativa en los conocimientos y comportamientos relacionados con la fotoprotección durante la práctica de la actividad física en los estudiantes de un colegio público de Bogotá D.C., Colombia

Introducción: La incidencia del cáncer de piel melanoma y no melanoma es un problema de salud pública a nivel mundial. El incremento en la incidencia del cáncer de piel en los últimos años se debe a múltiples factores como: cambios en los estilos de vida, el envejecimiento de la población, cambios ambientales, el desconocimiento a la exposición a la radiación ultravioleta (RUV) durante la práctica de actividad física sin elementos de fotoprotección, siendo éste último reconocido como el principal factor de riesgo. Objetivo: Evaluar los efectos de una intervención educativa en los conocimientos y comportamientos relacionados con la fotoprotección durante la práctica de la actividad física en estudiantes de un colegio público de Bogotá D.C., Colombia. Métodos: Estudio de intervención, antes y después, no controlado en 281 estudiantes de los grados noveno, décimo y once de estratos 1-3 de un colegio público de Bogotá, con seguimiento a 1, 3 y 6 meses post-intervención. Se evaluaron los conocimientos y los hábitos de fotoprotección mediante un cuestionario Cancer Awareness Measure (CAM) y el modelo Transteórico de cambio comportamental de Prochaska y Di Clemente. El estudio se realizó durante el primer semestre de 2015 con 4 sesiones educativas de 60 minutos apoyadas con material audiovisual y pedagógico, acorde a la Guía para la Comunicación Educativa en el marco el control del cáncer publicada por el Instituto Nacional de Cancerología. Resultados: Del grupo de estudiantes que participaron del estudio, el 52,3% eran hombres, el promedio de edad fue de 15,46 ± 1,2 años. El tipo de piel predominante fue la trigueña con 65,8%. La intervención educativa produjo cambios significativos en los conocimientos de foto protección, finalizado el seguimiento al sexto mes. En cuanto a la prevención los estudiantes refirieron tener conocimiento de cómo examinar su piel en el momento basal (12,5% n=35), presentándose un aumento significativo de 62,6% (n=211) al sexto mes (p<0,05). Conclusión: El estudio demostró la efectividad de la intervención educativa, evidenciando cambios significativos en los conocimientos en fotoprotección y comportamientos preventivos del cáncer de piel durante la práctica de la actividad física en estudiantes de un colegio público de Bogotá D.C., Colombia.

El videojuego League of legends y su efecto en memoria de trabajo visual y solución de problemas

El propósito de este estudio es medir los efectos que tiene el videojuego League of Legends en los procesos cognitivos de memoria de trabajo visual (MVT) y solución de problemas (SP). Para medir dichos efectos se implementó un diseño pre test-post con un grupo experimental y uno control, compuestos cada uno por siete participantes, en donde se evaluaron los procesos previamente mencionados utilizando los cubos de Corsi para MVT y las matrices del WAIS III para SP. Después de realizar los respectivos entrenamientos se encontraron resultados significativos en los diferentes momentos de aplicación. En el grupo experimental se encontraron diferencias en la variable dependiente SP, mientras que en el grupo control en MVT, pero no en la interacción entre grupos ni diferencias entre grupos, lo que sugiere un efecto de familiarización a la prueba.

O impacto da formação para o desenvolvimento pessoal e organizacional na CGD, S.A.

De modo a terminar o mestrado em Gestão, especialização em Recursos Humanos, considerou-se pertinente a realização de um estágio em contexto laboral, como forma a melhorar as competências técnicas e comportamentais. O estágio teve dois objectivos: aplicação dos conhecimentos adquiridos na primeira fase do mestrado e pesquisa de uma oportunidade de inserção no mercado de trabalho. O mesmo foi realizado na Caixa Geral Depósitos (CGD), empresa escolhida devido à posição de destaque que assume em Portugal e por se considerar que é uma organização líder e forte em muitos aspectos, nomeadamente em áreas da gestão de recursos humanos. Apesar das múltiplas abordagens que a gestão de recursos humanos tem e devido à especialização existente na CGD nesta área, o estágio foi essencialmente centrado no âmbito da formação. Considera-se que trabalhar nessa área trará bastantes beneficias pessoais uma vez que me dotará de competências fundamentais para enfrentar a realidade empresarial actual, que é fortemente acentuada na gestão do conhecimento. ABSTRACT: ln order to finish the master degree in Management, specializing in Human Resources, a pertinent stage-based learning was considered as a way to improve technical and behavioral skills. This stage had two targets: application of the obtained knowledge during the first stage of the master degree, and search for an opportunity of entering the labor market. This same stage was done at Caixa Geral Depósitos (CGD), a company that was chosen because of its prominent position assumed in Portugal, and is also considered a leading and strong organization in many areas, particularly human resource management. Despite the multiple approaches that human resources management has, and due to the know-how of CGD in this area, the stage was mainly focused in the training. It is considered that work in this area will bring many personal benefits as it will give me fundamental skills to deal with the actual business reality, which is strongly marked in knowledge management.

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

A deep learning-based approach for 3D people tracking

Questa tesi si occupa dell’estensione di un framework software finalizzato all'individuazione e al tracciamento di persone in una scena ripresa da telecamera stereoscopica. In primo luogo è rimossa la necessità di una calibrazione manuale offline del sistema sfruttando algoritmi che consentono di individuare, a partire da un fotogramma acquisito dalla camera, il piano su cui i soggetti tracciati si muovono. Inoltre, è introdotto un modulo software basato su deep learning con lo scopo di migliorare la precisione del tracciamento. Questo componente, che è in grado di individuare le teste presenti in un fotogramma, consente ridurre i dati analizzati al solo intorno della posizione effettiva di una persona, escludendo oggetti che l’algoritmo di tracciamento sarebbe portato a individuare come persone.

Detecting social patterns within 20th century documentary photos: a deep learning based approach

The job of a historian is to understand what happened in the past, resorting in many cases to written documents as a firsthand source of information. Text, however, does not amount to the only source of knowledge. Pictorial representations, in fact, have also accompanied the main events of the historical timeline. In particular, the opportunity of visually representing circumstances has bloomed since the invention of photography, with the possibility of capturing in real-time the occurrence of a specific events. Thanks to the widespread use of digital technologies (e.g. smartphones and digital cameras), networking capabilities and consequent availability of multimedia content, the academic and industrial research communities have developed artificial intelligence (AI) paradigms with the aim of inferring, transferring and creating new layers of information from images, videos, etc. Now, while AI communities are devoting much of their attention to analyze digital images, from an historical research standpoint more interesting results may be obtained analyzing analog images representing the pre-digital era. Within the aforementioned scenario, the aim of this work is to analyze a collection of analog documentary photographs, building upon state-of-the-art deep learning techniques. In particular, the analysis carried out in this thesis aims at producing two following results: (a) produce the date of an image, and, (b) recognizing its background socio-cultural context,as defined by a group of historical-sociological researchers. Given these premises, the contribution of this work amounts to: (i) the introduction of an historical dataset including images of “Family Album” among all the twentieth century, (ii) the introduction of a new classification task regarding the identification of the socio-cultural context of an image, (iii) the exploitation of different deep learning architectures to perform the image dating and the image socio-cultural context classification.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.

Novel Machine-Learning Based IC Testing Strategy

The final goal of the thesis should be a real-world application in the production test data environment. This includes the pre-processing of the data, building models and visualizing the results. To do this, different machine learning models, outlier prediction oriented, should be investigated using a real dataset. Finally, the different outlier prediction algorithms should be compared, and their performance discussed.

A Distributed Support based on Kubeflow to Ease MLOps of Federated Learning Algorithms

The main objective of my thesis work is to exploit the Google native and open-source platform Kubeflow, specifically using Kubeflow pipelines, to execute a Federated Learning scalable ML process in a 5G-like and simplified test architecture hosting a Kubernetes cluster and apply the largely adopted FedAVG algorithm and FedProx its optimization empowered by the ML platform ‘s abilities to ease the development and production cycle of this specific FL process. FL algorithms are more are and more promising and adopted both in Cloud application development and 5G communication enhancement through data coming from the monitoring of the underlying telco infrastructure and execution of training and data aggregation at edge nodes to optimize the global model of the algorithm ( that could be used for example for resource provisioning to reach an agreed QoS for the underlying network slice) and after a study and a research over the available papers and scientific articles related to FL with the help of the CTTC that suggests me to study and use Kubeflow to bear the algorithm we found out that this approach for the whole FL cycle deployment was not documented and may be interesting to investigate more in depth. This study may lead to prove the efficiency of the Kubeflow platform itself for this need of development of new FL algorithms that will support new Applications and especially test the FedAVG algorithm performances in a simulated client to cloud communication using a MNIST dataset for FL as benchmark.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.