Persona:
Gaudioso Vázquez, Elena

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0000-0003-2258-6623
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Gaudioso Vázquez
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Elena
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2020 - 20232
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search.filters.applied.f.accessLevel: Atribución 4.0 Internacional × Fecha de inicio: 2020 ×

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Mostrando 1 - 2 de 2
  • 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
    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.