Predicción y análisis de interacciones de usuarios en plataformas de enseñanza online

Las plataformas de enseñanza online generan gran cantidad de metadatos sobre las interacciones entre los estudiantes y con la plataforma. Esta información puede ser aprovechada por los profesores de los cursos para mejorar el curso y la experiencia docente de los estudiantes. En este contexto el objetivo de este TFG es el análisis de las interacciones realizadas por los estudiantes en cursos online y la predicción del comportamiento del estudiante utilizando su patrón de acceso a la plataforma. Debido al volumen de datos que se maneja se hará uso herramientas de computación en paralelo como Apache Spark para preprocesar los datos generados por la plataforma. Mediante Apache Spark se creará una aplicación que extraiga el patrón de acceso de los estudiantes a la plataforma y disminuya la gran cantidad de metadatos generada en un curso online. Por último, se aplicarán algoritmos de aprendizaje automático para predecir variables de interés sobre la interacción de los estudiantes con el curso como la probabilidad de abandono o el rendimiento académico. Esto también se realizará con la herramienta Apache Spark. En concreto, se utilizará el algoritmo Random Forest de la librería MLlib de Spark con la finalidad de obtener el mejor resultado a la hora de predecir las variables de interés del curso.Online education platforms generate a lot of metadata about interactions among students and with the platform. This information can be harnessed by teachers to improve the course and student’s teaching experience. In this context the aim of this study is the analysis of interactions performed by students and the prediction of student’s behavior using his access patterns to platform. Due to the volume of data handled, we use a tool for parallel computing such as Apache Spark for preprocessing the data generated by the platform. We create an application that extracts the access patterns to platform and decreases the volume of the metadata generated in this online course. Finally, we apply machine learning algorithms to predict target variables related to the interactions of students enrolled in the course, for example the dropout rate or the academic performance. We also use the tool Apache Spark for this task. Specifically, we apply the algorithm Random Forest from the library MLlib in order to get the best result in predicting the course’s target variables

Structuring Pt/CeO2/Al2O3 WGS catalyst: Introduction of buffer layer

This work is devoted to the development of novel structured catalytic system for WGS reaction. The new concept is related to the presence of a pre-catalytic “buffer” layer formed by WGS-inert oxide, i.e. not involved in CO conversion, but able to increase the number of participating sites in water dissociation step during the reaction. The performance of the proposed systems appears to depend strongly on the stream composition, being its effect beneficial in highly reducing atmospheres making it ideal for clean-up application. An increment of the partial kinetic order for water species is observed and reveals the key role of the water activation for superior catalytic behavior.Junta de Andalucía TEP-819

Monitoring the Reaction Mechanism in Model Biogas Reforming by In Situ Transient and Steady-State DRIFTS Measurements

In this work, the reforming of model biogas was investigated on a Rh/MgAlO catalyst. In situ transient and steady-state diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements were used to gain insight into the reaction mechanism involved in the activation of CH and CO. It was found that the reaction proceeds through of an initial pathway in which methane and CO are both dissociated on Rh metallic sites and additionally a bifunctional mechanism in which methane is activated on Rh sites and CO is activated on the basic sites of the support surface via a formate intermediate by H-assisted CO decomposition. Moreover, this plausible mechanism is able to explain why the observed apparent activation energy of CO is much lower than that of CH. Our results suggest that CO dissociation facilitates CH activation, because the oxygen-adsorbed species formed in the decomposition of CO are capable of reacting with the CH species derived from methane decomposition.Ministerio de Economía y Competitividad ENE2013-47880-C3-2-R, ENE2015-66975-C3-2-RJunta de Andalucía TEP-819

Galaxy classification: deep learning on the OTELO and COSMOS databases

Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sersic index or the concentration index. Methods. We used three classification methods for the OTELO database: 1) u-r color separation , 2) linear discriminant analysis using u-r and a shape parameter classification, and 3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data. Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog. Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged dataComment: 20 pages, 10 tables, 14 figures, Astronomy and Astrophysics (in press

Do Engineering Students Know How to Use Generative Artificial Intelligence? A Case Study

[EN]In the context of engineering education, the utilization of generative artificial intelligence (AI) holds significant promise for enhancing learning experiences. This article presents a compelling case study designed to assess the proficiency of engineering students in employing generative AI, particularly focusing on ChatGPT. Students from diverse engineering disciplines and academic levels engage in a knowledge questionnaire, with one group utilizing ChatGPT and the other leveraging unrestricted internet resources. The study not only investigates the effectiveness of generative AI as a learning tool but also explores its impact on problem-solving skills. Towards the end of the questionnaire, students are surveyed using a validated instrument to gauge their perceptions and experiences regarding the use of ChatGPT and generative AI in the realm of engineering education. This research contributes valuable insights into the integration of generative AI as a pedagogical tool, shedding light on its potential to shape the future of engineering instruction

El presidio del Canal de Isabel II en el contexto jurídico y penitenciario de la España isabelina (1851-1867)

El tema principal de esta tesis se enmarca dentro del mundo de las prisiones del siglo XIX, habiendo utilizado para ello un caso específico, el presidio del Canal de Isabel II, un establecimiento penal que permaneció vigente entre 1851 y 1867. La investigación se ha encaminado a través de una doble vertiente, cuyos objetivos se han dirigido a conocer, por un lado, la vida que llevaron a cabo los presidiarios en este lugar, habiendo sido necesario abordar diferentes puntos de vista, que guardan relación con los aspectos penitenciarios, penales, económicos, laborales, alimenticios y sanitarios; por otro lado, proponer un modelo de análisis de los presidios españoles de obras públicas del siglo XIX por medio de un estudio de caso, señalando todos aquellos aspectos que consideramos necesarios deben abordarse al acometer un estudio de este tipo. A través de ambos ejes principales, se ha llevado a cabo un estudio específico y exhaustivo sobre este centro, abordando una línea de trabajo que se aleja del marco más común en los estudios sobre prisiones..

Análisis, comparación y predicción de interacciones de usuarios en cursos online

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The OTELO survey. A case study of [O III]4959,5007 emitters at <z> = 0.83

The OTELO survey is a very deep, blind exploration of a selected region of the Extended Groth Strip and is designed for finding emission-line sources (ELSs). The survey design, observations, data reduction, astrometry, and photometry, as well as the correlation with ancillary data used to obtain a final catalogue, including photo-z estimates and a preliminary selection of ELS, were described in a previous contribution. Here, we aim to determine the main properties and luminosity function (LF) of the [O III] ELS sample of OTELO as a scientific demonstration of its capabilities, advantages, and complementarity with respect to other surveys. The selection and analysis procedures of ELS candidates obtained using tunable filter (TF) pseudo-spectra are described. We performed simulations in the parameter space of the survey to obtain emission-line detection probabilities. Relevant characteristics of [O III] emitters and the LF([O III]), including the main selection biases and uncertainties, are presented. A total of 184 sources were confirmed as [O III] emitters at a mean redshift z=0.83. The minimum detectable line flux and equivalent width (EW) in this ELS sample are $\sim$5 $\times$ 10$^{-19}$ erg s$^{-1}$ cm$^{2}$ and $\sim$6 \AA, respectively. We are able to constrain the faint-end slope ($\alpha = -1.03\pm0.08$) of the observed LF([O III]) at z=0.83. This LF reaches values that are approximately ten times lower than those from other surveys. The vast majority (84\%) of the morphologically classified [O III] ELSs are disc-like sources, and 87\% of this sample is comprised of galaxies with stellar masses of M$_\star$ $<$ 10$^{10}$ M$_{\odot}$.Comment: v1: 16 pages, 6 figures. Accepted in Astronomy \& Astrophysics. v2: Author added in metadat

Charged-particle multiplicities in pp interactions at root s=900 GeV measured with the ATLAS detector at the LHC

22 páginas, 4 figuras, 1 tabla.-- et al.(ATLAS Collaboration).-- arXiv:1003.3124v2The first measurements from proton-proton collisions recorded with the ATLAS detector at the LHC are presented. Data were collected in December 2009 using a minimum-bias trigger during collisions at a centre-of-mass energy of 900 GeV. The charged-particle multiplicity, its dependence on transverse momentum and pseudorapidity. and the relationship between mean transverse momentum and charged-particle multiplicity are measured for events with at least one charged particle in the kinematic range vertical bar eta vertical bar 500 MeV. The measurements are compared to Monte Carlo models of proton-proton collisions and to results from other experiments at the same centre-of-mass energy. The charged-particle multiplicity per event and unit of pseudorapidity eta = 0 is measured to be 1.333 +/- 0.003(stat.) +/- 0.040(syst.), which is 5-15% higher than the Monte Carlo models predict.We are greatly indebted to all CERN’s departments and to the LHC project for their immense efforts not only in building the LHC, but also for their direct contributions to the construction and installation of the ATLAS detector and its infrastructure. All our congratulations go to the LHC operation team for the superb performance during this initial data-taking period. We acknowledge equally warmly all our technical colleagues in the collaborating Institutions without whom the ATLAS detector could not have been built. Furthermore we are grateful to all the funding agencies which supported generously the construction and the commissioning of the ATLAS detector and also provided the computing infrastructure. The ATLAS detector design and construction has taken about fifteen years, and our thoughts are with all our colleagues who sadly could not see its final realisation. We acknowledge the support of ANPCyT, Argentina; Yerevan Physics Institute, Armenia; ARC and DEST, Australia; Bundesministerium für Wissenschaft und Forschung, Austria; National Academy of Sciences of Azerbaijan; State Committee on Science & Technologies of the Republic of Belarus; CNPq and FINEP, Brazil; NSERC, NRC, and CFI, Canada; CERN; CONICYT, Chile; NSFC, China; COLCIENCIAS, Colombia; Ministry of Education, Youth and Sports of the Czech Republic, Ministry of Industry and Trade of the Czech Republic, and Committee for Collaboration of the Czech Republic with CERN; Danish Natural Science Research Council and the Lundbeck Foundation; European Commission, through the ARTEMIS Research Training Network; IN2P3-CNRS and Dapnia-CEA, France; Georgian Academy of Sciences; BMBF, HGF, DFG and MPG, Germany; Ministry of Education and Religion, through the EPEAEK program PYTHAGORAS II and GSRT, Greece; ISF, MINERVA, GIF, DIP, and Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; CNRST, Morocco; FOM and NWO, Netherlands; The Research Council of Norway; Ministry of Science and Higher Education, Poland; GRICES and FCT, Portugal; Ministry of Education and Research, Romania; Ministry of Education and Science of the Russian Federation and State Atomic Energy Corporation “Rosatom”; JINR; Ministry of Science, Serbia; Department of International Science and Technology Cooperation, Ministry of Education of the Slovak Republic; Slovenian Research Agency, Ministry of Higher Education, Science and Technology, Slovenia; Ministerio de Educación y Ciencia, Spain; The Swedish Research Council, The Knut and Alice Wallenberg Foundation, Sweden; State Secretariat for Education and Science, Swiss National Science Foundation, and Cantons of Bern and Geneva, Switzerland; National Science Council, Taiwan; TAEK, Turkey; The Science and Technology Facilities Council and The Leverhulme Trust, United Kingdom; DOE and NSF, United States of America.Peer reviewe