Persona:
Gaudioso Vázquez, Elena

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ORCID
0000-0003-2258-6623
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Gaudioso Vázquez
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Elena
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2020 - 20235
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Fecha de finalización: 2023 × Fecha de inicio: 2020 ×

Resultados de la búsqueda

Mostrando 1 - 5 de 5
  • Miniatura
    Publicación
    EVIN, una aplicación adaptativa integral para el entrenamiento visual online de niños con baja visión
    (International Journal of Developmental and Educational Psychology, 2021) Santos Plaza, Carlos Manuel; Matas Martín, Yolanda; Hernández del Olmo, Félix; Gaudioso Vázquez, Elena
    La baja visión es una deficiencia visual que no puede ser mejorada con ayudas ópticas convencionales. No obstante, para incrementar sus habilidades estas personas pueden seguir un programa de entrenamiento visual planificado y supervisado por un experto en este campo. Este entrenamiento es especialmente efectivo en niños, debido a su plasticidad para aprender. Pero, debido a la falta de expertos especializados, las sesiones de entrenamiento son generalmente menos frecuentes de lo que sería conveniente. Los programas de entrenamiento visual online son una solución para mitigar este problema, porque pueden ser llevado a efecto por expertos y familias de forma conjunta. De este modo, desarrollamos la aplicación Estimulación Visual en Internet (EVIN), que proporciona un programa de entrenamiento visual en diferentes tareas a través de juegos. Además, presenta informes de los resultados de los niños durante el entrenamiento. Aunque en trabajos anteriores ya se ha probado la utilidad de EVIN, se ha visto necesaria abordar dos nuevas metas: (i) proporcionar algún tipo de soporte en EVIN que ayude a expertos y familias a trabajar juntos debido, entre otras causas, a la gran variedad de ejercicios y configuraciones que pueden ser prescritas a los niños y, (ii) diseñar un riguroso experimento para comparar el entrenamiento visual en niños con EVIN con el entrenamiento con métodos tradicionales. Para afrontar estos objetivos, presentamos una versión adaptativa de EVIN que proporciona una nueva herramienta que permite al experto planificar el entrenamiento visual usando plantillas de ejercicios prediseñadas. Además, hemos desarrollado nuevas métricas e informes que permiten valorar con mayor precisión los resultados de los niños. Todo ello nos ha permitido desarrollar un experimento para evaluar si se produce mejora significativa en los niños entrenados con EVIN.
  • Miniatura
    Publicación
    Advanced Control by Reinforcement Learning for Wastewater Treatment Plants: A Comparison with Traditional Approaches
    (MDPI, 2023) Gorrotxategi Zipitria, Mikel; Hernández del Olmo, Félix; Gaudioso Vázquez, Elena; Duro Carralero, Natividad; Dormido Canto, Raquel
    Control mechanisms for biological treatment of wastewater treatment plants are mostly based on PIDS. However, their performance is far from optimal due to the high non-linearity of the biological and changing processes involved. Therefore, more advanced control techniques are proposed in the literature (e.g., using artificial intelligence techniques). However, these new control techniques have not been compared to the traditional approaches that are actually being used in real plants. To this end, in this paper, we present a comparison of the PID control configurations currently applied to control the dissolved oxygen concentration (in the active sludge process) against a reinforcement learning agent. Our results show that it is possible to have a very competitive operating cost budget when these innovative techniques are applied.
  • Miniatura
    Publicación
    Supporting Teachers to Monitor Student’s Learning Progress in an Educational Environment With Robotics Activities
    (IEEE, 2020-03-06) Orlando, Samantha; Gaudioso Vázquez, Elena; Paz López, Félix de la
    Educational robotics has proven its positive impact on the performances and attitudes of students. However, the educational environments that employ them rarely provide teachers with relevant information that can be used to make an effective monitoring of the student learning progress. To overcome these limitations, in this paper we present IDEE (Integrated Didactic Educational Environment), an educational environment for physics, that uses EV3 LEGO Mindstorms R© educational kit as robotic component. To provide support to teachers, IDEE includes a dashboard that provides them with information about the students’ learning process. This analysis is done by means of an Additive Factor Model (AFM). That is a well-known technique in the educational data mining research area. However, it has been usually employed to carry out analysis about students’ performance data outside the system. This can be a burden for the teacher who, in most cases, is not an expert in data analysis. Our goal in this paper is to show how the coefficients of AFM provide valuable information to the teacher without requiring any deep expertise in data analysis. In addition, we show an improved version of the AFM that provides a deeper understanding about the students’ learning process.
  • Miniatura
    Publicación
    Toward Embedding Robotics in Learning Environments With Support to Teachers: The IDEE Experience
    (IEEE, 2023-12-06) Orlando, Samantha; Gaudioso Vázquez, Elena; Paz López, Félix de la
    Nowadays, there is an increasing interest in using different technologies, such as educational robotics in classrooms. However, in many cases, teachers have neither the necessary background to efficiently use these kits nor the information about how students are using robotics in classroom. To support teachers, learning environments with robotics tools should monitor the students’ interaction data while they are interacting with the different resources provided. With the analysis of this data, teachers can obtain valuable information about students’ learning progress. In previous work, we presented integrated didactic educational environment (IDEE), an integrated learning environment that uses robotics to support physics laboratories in secondary education. Students’ interactions with IDEE are stored and analyzed using the additive factor model to show the teachers the most significant skills in the learning process and those students who have difficulties with these skills. Now, our goal is to enhance the information given to the teachers to allow them to focus on the specific needs of each student on every different skill involved in the activities and not only the significant skills. To this end, we use a conjunctive knowledge tracing model based on a hidden Markov model. In this article: first, we describe how the CKT model has been adapted to the pedagogical model of IDEE, second, we show that this model can identify the skills that each student masters, and thus, support teachers in identifying learning criticalities in students.
  • Miniatura
    Publicación
    Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome
    (BioMed Central (Springer), 2022) Madrigal, Pedro; Singh, Nitin K.; Wood, Jason M.; Mason, Christopher E.; Venkateswaran, Kasthuri; Beheshti, Afshin; Gaudioso Vázquez, Elena; Hernández del Olmo, Félix
    Background: Antimicrobial resistance (AMR) has a detrimental impact on human health on Earth and it is equally concerning in other environments such as space habitat due to microgravity, radiation and confinement, especially for long-distance space travel. The International Space Station (ISS) is ideal for investigating microbial diversity and virulence associated with spaceflight. The shotgun metagenomics data of the ISS generated during the Microbial Tracking–1 (MT-1) project and resulting metagenome-assembled genomes (MAGs) across three flights in eight different locations during 12 months were used in this study. The objective of this study was to identify the AMR genes associated with whole genomes of 226 cultivable strains, 21 shotgun metagenome sequences, and 24 MAGs retrieved from the ISS environmental samples that were treated with propidium monoazide (PMA; viable microbes). Results: We have analyzed the data using a deep learning model, allowing us to go beyond traditional cut-offs based only on high DNA sequence similarity and extending the catalog of AMR genes. Our results in PMA treated samples revealed AMR dominance in the last flight for Kalamiella piersonii, a bacteria related to urinary tract infection in humans. The analysis of 226 pure strains isolated from the MT-1 project revealed hundreds of antibiotic resistance genes from many isolates, including two top-ranking species that corresponded to strains of Enterobacter bugandensis and Bacillus cereus. Computational predictions were experimentally validated by antibiotic resistance profiles in these two species, showing a high degree of concordance. Specifically, disc assay data confirmed the high resistance of these two pathogens to various beta-lactam antibiotics. Conclusion: Overall, our computational predictions and validation analyses demonstrate the advantages of machine learning to uncover concealed AMR determinants in metagenomics datasets, expanding the understanding of the ISS environmental microbiomes and their pathogenic potential in humans.