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Dynamic strategy to promote significant learning at Universitybibliuned:425-Jmsaez-0013Este artículo muestra los resultados fundamentales de la investigación sobre el trabajo didáctico con las tabletas en la educación superior. En la actual era digital, en el mundo de las Tics y en la Sociedad del Conocimiento, las tabletas usadas en fo03412<a class="citation_author_name" title="Navegar por nombre de Autor de Sevillano García, María Luisa" href="/fez/list/author/Sevillano García, María Luisa/">Sevillano García, María Luisa</a>, <a class="citation_author_name" title="Navegar por nombre de Autor de Espinel De Segura, Blanca Inés" href="/fez/list/author/Espinel De Segura, Blanca Inés/">Espinel De Segura, Blanca Inés</a>, <a class="citation_author_name" title="Navegar por nombre de Autor de Sáez López, José Manuel" href="/fez/list/author/Sáez López, José Manuel/">Sáez López, José Manuel</a> y <a class="citation_author_name" title="Navegar por nombre de Autor de Sánchez Romero, Cristina" href="/fez/list/author/Sánchez Romero, Cristina/">Sánchez Romero, Cristina</a> . (<span class="citation_date">2020</span>) <a class="citation_title" title="Click para ver : Tablet devices. Dynamic strategy to promote significant learning at University" href="/fez/view/bibliuned:425-Jmsaez-0013">Tablet devices. Dynamic strategy to promote significant learning at University</a>. RecordArtículo de revistaPublishedEducaciónUniversidad de Sevilla (España). Grupo de Investigación Didáctica (HUM-390)Sevillano García, María LuisaEspinel De Segura, Blanca InésSáez López, José ManuelSánchez Romero, Cristina1Pixel-Bit: Revista de medios y educaciónbibliuned:425-Jmsaez-0013http://e-spacio.uned.es/fez/view/bibliuned:425-Jmsaez-00139712359engLa tableta. Estrategia dinámica para favorecer el aprendizaje significativo universitarioSaez_Lopez_Jose_M_La_tableta_Estrategia_2020.pdfpresmd_Saez_Lopez_Jose_M_La_tableta_Estrategia_2020.xml1133-8482bibliuned:425bibliuned:Setarticulobibliuned:SetopenaireDepartamento de Didáctica, Organización Escolar y Didácticas Especiales (UNED). ArtículosSet de artículoSet de openairehttp://creativecommons.org/licenses/by-nc-nd/4.0Licencia Creative CommonsSevillano GarcíaAcceso abiertohttps://doi.org/10.12795/pixelbit.774071.020202331982020-06-01T00:00:00Z3962021-09-20T21:49:28Z2021-09-20T21:49:28ZCapturing, Modelling, Analyzing and providing Feedback in Martial Arts with Artificial Intelligence to support Psychomotor Learning Activitiesbibliuned:master-ETSInformatica-IAA-AcasasThis Master’s Thesis explores how Artificial Intelligence (IA) can assist in the learning of psychomotor activities, specifically the learning of a martial art, through the development of an AI-based application that executes in an Android device. Martial arts are an interesting domain for this because it encompasses most of the characteristics that can be found in other psychomotor activities. Different methods for capturing, modelling and analyzing human motion, as well as providing feedback to the user have been reviewed. In addition, another bibliographical review of 27 publications has been carried out to evaluate till what extend these methods have been already applied to martial arts. For this research work, inertial methods have been selected for capturing motion. In particular, the inertial sensors of an Android device have been used for capturing the execution of a set of movements of American Kenpo Karate from 20 volunteers. The captured data was then modeled, by segmenting and labelling the movements, and smoothing the time series using Exponentially Weighted Moving Averages. The resulting dataset, formed by 240 movements, was then used for training and comparing three neural network-based classifiers: FC-ANN, 1D-CNN and LSTM. Neural networks were selected because of their ability of learn complex functions and the fact that some neural network architectures have been created specifically for analyze time series. Further, the weights learned by a neural network can be transferred to other domains through the technique known as transfer learning. Obtained results suggest that LSTM is the type of neural network that can better classify the movements studied, obtaining an accuracy of 1.0 in the training set, and an accuracy of 0.94 in the testing set. For demonstrating that those methods can be applied, an AI-based real-time Android application has been developed. This application employs the studied methods, as well as a feedback strategy created using the results of a questionnaire carried out with the purpose of identifying the issues that online learning of a psychomotor activity can entails. The application has then been tested, generating a good impression in the users. Following the open science philosophy, all contributions are shared in the GitHub repository.0Doctoral Thesis5232<a class="citation_author_name" title="Navegar por nombre de Autor de Casas Ortiz, Alberto" href="/fez/list/author/Casas Ortiz, Alberto/">Casas Ortiz, Alberto</a>. (<span class="citation_date">2020</span>). <i><a class="citation_title" title="Click para ver : Capturing, Modelling, Analyzing and providing Feedback in Martial Arts with Artificial Intelligence to support Psychomotor Learning Activities" href="/fez/view/bibliuned:master-ETSInformatica-IAA-Acasas">Capturing, Modelling, Analyzing and providing Feedback in Martial Arts with Artificial Intelligence to support Psychomotor Learning Activities</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.Casas Ortiz, AlbertoSantos, Olga C.bibliuned:master-ETSInformatica-IAA-Acasashttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-AcasasengCasas_Ortiz_Alberto_TFM.pdfpresmd_Casas_Ortiz_Alberto_TFM.xmlbibliuned:master-ETSInformatica-IAAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones (UNED)Set de items trabajo fin de másterSet de openaireCasas OrtizAcceso abierto0.905485431982020-03-06T00:00:00Z5922021-09-20T21:03:06Z2021-09-20T21:52:08ZThermographic Breast Cancer Detection. Deep Learning with a Small Datasetbibliuned:master-ETSInformatica-IAA-AsafontAccording to the World Health Organization (WHO), breast carcinoma is the cancer with highest prevalence among women, with 2.1 million new diagnoses every year. Given the risk of death associated to the metastasis during the late stages of the cancer, early detection is the optimal strategy to reduce the risk of death. Among the numerous tests that can be used in the breast cancer screening, thermography represents a non-invasive, painless, and free of ionizing radiation. The research group within which I have done this research is interested in applying artificial intelligence to analyzing thermographic images for breast cancer screening. Given that the project that this group intends to carry out in collaboration with HM Hospitales has not yet begun, we have used in this master thesis the Database for Mastology Research (DMR) developed at the Visual Lab of the Universidade Federal Fluminense, in Brazil, which is the only dataset of breast thermograms publicly available. It contains 216 patients, with up to 25 image per patient. It has been studied in dozens of research works, most of them using statistical feature extraction and machine learning algorithms for classification. Unfortunately this database has important flaws, such as two different patient having exactly the same image (pixel by pixel), which have not been mentioned in previous works. For this reason we have devoted a significant effort to cleaning the dataset, which reduced it to only 188 images. We have then tried several deep learning models for image classification. We first built from scratch several Convolutional Neural Networks (CNNs), each consisting of n pairs of convolutional-maxpool layers, a flatten layer, and n dense layers, for different values of n. All the CNNs gave poor results: the highest accuracy, obtained for n = 4, was 75%, and the largest area under the ROC (AUC), obtained for n = 5, was 0.70. We also took into account that a false positive, which may cause anxiety and discomfort to the patient and lead to a biopsy, is not as serious as a false positive, which may delay the detection of cancer, thus requiring more aggressive and expensive treatments and drastically reducing the survival rate. After consulting with a radiologist of HM Montepríncipe hospital, we estimate that the relative cost of a false negative is at least 20 times higher than that of a false positive and defined a metric in which a false negative weighs the same as 20 false positives. In our study, the CNN with n = 5 has the smallest weighted error, by far, so we have selected this network as a reference for the next phases of our study. In the second group of experiments we have used three of the most popular pre-trained CNNs available in Keras: VGG16, VGG19, and ResNet50, and optimized their parameters for our dataset; this process is usually called transfer learning. Contrary to other results published in the literature, all these re-trained CNNs performed worse than the optimal network built from scratch, i.e., the one with n = 5. Finally, we have built several hybrid models by replacing the top m layers of the optimal CNN with either a Support Vector Machine (SVM) or a Sum-Product Network (SPN), for different values of m. Again the performance was lower than for the optimal pure CNN. The conclusion is that when the dataset contains a relatively small number of images, large CNNs tend to overfit, thus leading to poor AUCs, contrary to the case of large datasets, for which very deep networks usually perform much better than shallow ones. An additional reason for which transfer learning did not work in our study is that the above-mentioned networks were trained for color images, while in a thermogram every pixel does not represent a red-green-blue (RGB) color, but a temperature, and for this reason in our case the networks built from scratch (at least some of them) performed better than re-trained CNNs.0Doctoral Thesis3602<a class="citation_author_name" title="Navegar por nombre de Autor de Safont Andreu, Anna" href="/fez/list/author/Safont Andreu, Anna/">Safont Andreu, Anna</a>. (<span class="citation_date">2020</span>). <i><a class="citation_title" title="Click para ver : Thermographic Breast Cancer Detection. Deep Learning with a Small Dataset" href="/fez/view/bibliuned:master-ETSInformatica-IAA-Asafont">Thermographic Breast Cancer Detection. Deep Learning with a Small Dataset</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.Safont Andreu, AnnaSánchez Cauce, RaquelDíez Vegas, Francisco Javierbibliuned:master-ETSInformatica-IAA-Asafonthttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-AsafontengSafont_Andreu_Anna_TFM.pdfpresmd_Safont_Andreu_Anna_TFM.xmlbibliuned:master-ETSInformatica-IAAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones (UNED)Set de items trabajo fin de másterSet de openaireSafont AndreuAcceso abierto0.76362812223322322