Calidad de los sistemas educativos: Modelos de evaluación

The Psychometric Research Group of the University of Oviedo participated in different presentations and workshops organized within the framework of the II International Congress of Psychological Evaluation held in November 2018 at the San Ignacio De Loyola University (Lima, Peru). This work gathers part of those contributions. Specifically, the aim of this paper is to present the mathematical models and methodological procedures available for the analysis of data in the evaluation of educational systems. The design, execution and dissemination of the results of an evaluation program of the education system is a complex task that poses a challenge in different areas, among which data analysis stands out. These programs have two main purposes: to know and describe the level of knowledge and skills of the student population and to identify and analyze the context and process factors associated with educational outcomes. In order to fulfil both purposes, the evaluation of education systems has been provided with unique and specific methodological solutions. Three of them are presented in this paper. Two are aimed at expressing learning outcomes: plausible values and cut-off methods, while the last focuses on analyzing the relationship between school factors and outcomes.El grupo de Investigación Psicometría de la Universidad de Oviedo participó en diferentes ponencias y talleres organizados en el marco del II Congreso Internacional de Evaluación Psicológica celebrado en noviembre de 2018 en la Universidad San Ignacio De Loyola (Lima, Perú). El presente trabajo recoge parte de aquellas aportaciones. En concreto el objetivo de este escrito es presentar los modelos matemáticos y procedimientos metodológicos disponibles para el análisis de los datos en las evaluaciones de sistemas educativos. El diseño, ejecución y diseminación de resultados de un programa de evaluación de sistema educativo es una tarea compleja que supone un desafío en diferentes ámbitos, entre los que destaca el análisis de datos. Estos programas tienen dos grandes finalidades: conocer y describir el nivel de conocimientos y competencias de la población de estudiantes e identificar y analizar los factores de contexto y proceso asociados a los resultados educativos. Para cumplir ambas finalidades la evaluación de sistemas educativos se ha dotado de soluciones metodológicas singulares y específicas. En este escrito se presentan tres de ellas. Dos están orientadas a expresar los resultados del aprendizaje: valores plausibles y métodos de punto de corte, mientras que la última está centrada en analizar la relación entre los factores escolares y los resultados

Calidad de los sistemas educativos: Modelos de evaluación

The Psychometric Research Group of the University of Oviedo participated in different presentations and workshops organized within the framework of the II International Congress of Psychological Evaluation held in November 2018 at the San Ignacio De Loyola University (Lima, Peru). This work gathers part of those contributions. Specifically, the aim of this paper is to present the mathematical models and methodological procedures available for the analysis of data in the evaluation of educational systems. The design, execution and dissemination of the results of an evaluation program of the education system is a complex task that poses a challenge in different areas, among which data analysis stands out. These programs have two main purposes: to know and describe the level of knowledge and skills of the student population and to identify and analyze the context and process factors associated with educational outcomes. In order to fulfil both purposes, the evaluation of education systems has been provided with unique and specific methodological solutions. Three of them are presented in this paper. Two are aimed at expressing learning outcomes: plausible values and cut-off methods, while the last focuses on analyzing the relationship between school factors and outcomes.El grupo de Investigación Psicometría de la Universidad de Oviedo participó en diferentes ponencias y talleres organizados en el marco del II Congreso Internacional de Evaluación Psicológica celebrado en noviembre de 2018 en la Universidad San Ignacio De Loyola (Lima, Perú). El presente trabajo recoge parte de aquellas aportaciones. En concreto el objetivo de este escrito es presentar los modelos matemáticos y procedimientos metodológicos disponibles para el análisis de los datos en las evaluaciones de sistemas educativos. El diseño, ejecución y diseminación de resultados de un programa de evaluación de sistema educativo es una tarea compleja que supone un desafío en diferentes ámbitos, entre los que destaca el análisis de datos. Estos programas tienen dos grandes finalidades: conocer y describir el nivel de conocimientos y competencias de la población de estudiantes e identificar y analizar los factores de contexto y proceso asociados a los resultados educativos. Para cumplir ambas finalidades la evaluación de sistemas educativos se ha dotado de soluciones metodológicas singulares y específicas. En este escrito se presentan tres de ellas. Dos están orientadas a expresar los resultados del aprendizaje: valores plausibles y métodos de punto de corte, mientras que la última está centrada en analizar la relación entre los factores escolares y los resultados

Autorregulación en el mercado de fútbol peruano: fair play financiero y el caso de Alianza Lima ¿Inversión o utopía?

El mercado económico del fútbol en el Perú no cuenta con una regulación clara y coherente con el desarrollo del deporte a nivel internacional. La economía nacional mantiene un crecimiento constante desde hace más de 20 años; esto, naturalmente, antes de la llegada de la pandemia por COVID-19 desde marzo del 2020. Este auge, el cual se ve reflejado en todos los sectores económicos del país, no puede ser ajeno al sector de entretenimiento, en el cual se incluye el sector deportivo, particularmente al fútbol. Sin embargo, a pesar de ser una potencia por explotar, en términos económicos, no se encuentra una correlación óptima entre los gastos generados por los clubes y los ingresos generados por los mismos. La presente investigación tiene como objetivo desarrollar un estudio crítico acerca de estas variables y proponer soluciones orientadas a la profesionalización del deporte a través de la autorregulación financiera. El periodo de análisis para estas variables en el fútbol nacional será desde el inicio del procedimiento concursal en el 2013 al 2020. Se plantea que la relación asociada al mundo deportivo, “a mayor gasto, mejores resultados deportivos”, es errónea; los gastos sin una autorregulación financiera óptima resultan perjudiciales tanto en el corto como en el largo plazo, pudiendo llevar a la institución deportiva a su liquidación. En esta investigación se usa un modelo Pooled Ordinary Least Squares a fin de estimar los desempeños deportivos y desempeños financieros de los clubes deportivos concursados, y así determinar la existencia o no de la correlación anteriormente mencionada

A Deep Learning Approach for Molecular Classification Based on AFM Images

In spite of the unprecedented resolution provided by non-contact atomic force microscopy (AFM) with CO-functionalized and advances in the interpretation of the observed contrast, the unambiguous identification of molecular systems solely based on AFM images, without any prior information, remains an open problem. This work presents a first step towards the automatic classification of AFM experimental images by a deep learning model trained essentially with a theoretically generated dataset. We analyze the limitations of two standard models for pattern recognition when applied to AFM image classification and develop a model with the optimal depth to provide accurate results and to retain the ability to generalize. We show that a variational autoencoder (VAE) provides a very efficient way to incorporate, from very few experimental images, characteristic features into the training set that assure a high accuracy in the classification of both theoretical and experimental images

Molecular Identification from AFM Images Using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks

Spectroscopic methods like nuclear magnetic resonance, mass spectrometry, X-ray diffraction, and UV/visible spectroscopies applied to molecular ensembles have so far been the workhorse for molecular identification. Here, we propose a radically different chemical characterization approach, based on the ability of noncontact atomic force microscopy with metal tips functionalized with a CO molecule at the tip apex (referred as HRAFM) to resolve the internal structure of individual molecules. Our work demonstrates that a stack of constant-height HR-AFM images carries enough chemical information for a complete identification (structure and composition) of quasiplanar organic molecules, and that this information can be retrieved using machine learning techniques that are able to disentangle the contribution of chemical composition, bond topology, and internal torsion of the molecule to the HR-AFM contrast. In particular, we exploit multimodal recurrent neural networks (M-RNN) that combine convolutional neural networks for image analysis and recurrent neural networks to deal with language processing, to formulate the molecular identification as an imaging captioning problem. The algorithm is trained using a data set which contains almost 700,000 molecules and 165 million theoretical AFM images to produce as final output the IUPAC name of the imaged molecule. Our extensive test with theoretical images and a few experimental ones shows the potential of deep learning algorithms in the automatic identification of molecular compounds by AFM. This achievement supports the development of on-surface synthesis and overcomes some limitations of spectroscopic methods in traditional solution-based synthesisWe would like to acknowledge support from the Comunidad de Madrid Industrial Doctorate programme 2017 under reference number IND2017/IND7793 and from Quasar Science Resources S.L. P.P. and R.P. acknowledge support from the Spanish Ministry of Science and Innovation, through project PID2020-115864RB-I00 and the “María de Maeztu” Programme for Units of Excellence in R&D (CEX2018-000805-M). C.R.-M. acknowledges financial support by the Ramón y Cajal program of the Spanish Ministry of Science and Innovation (ref. RYC2021-031176-I). Computer time provided by the Red Española de Supercomputación (RES) at the Finisterrae II Supercomputer is also acknowledge

Estimación y evaluación de modelos estructurales centro-periferia

Resulta revelador intentar plasmar la idea referida a un conglomerado de agentes que constituyan un núcleo alrededor del cual gire la actividad objeto de estudio. La concepción de una estructura formada por un centro y una periferia, constituye un paradigma clásico y recurrente en muchos campos de la ciencia. Siguiendo esta línea, los investigadores Stephen Borgatti y Martin Everett desarrollan un modelo estructural en 1999 basado en la delimitación de un centro formado por un conjunto de actores fuertemente relacionados, esto es, un grupo cohesivo y con alta densidad de interrelaciones. En contraposición, los agentes dispersos y poco conectados de la red delimitan la periferia del sistema. El enfoque original de los autores es modificado empleando medidas que creemos, aportan un mayor grado de coherencia y exactitud a los objetivos planteados. En este trabajo, sin perdida de generalidad nos centramos en la estimación y posteriormente en la evaluación, de modelos centro-periferia basados en grafos valorados.The conception of a structure made up of a core and periphery is a common, classic paradigm in many fields of science. Following this line, in 1999 researchers Stephen Borgatti and Martin Everett developed a model of structural analysis based on the delimitation of a core formed by a group of densely connected actors, in contrast to a class of actors, more loosely connected and forming the periphery of the system. The original approach of these authors is modified, employing measures that, in our opinion, show a larger degree of coherence and accuracy in the proposed objectives

Substrate-induced enhancement of the chemical reactivity in metal-supported graphene

Graphene is commonly regarded as an inert material. However, it is well known that the presence of defects or substitutional hetero-atoms confers graphene promising catalytic properties. In this work, we use first-principles calculations to show that it is also possible to enhance the chemical reactivity of a graphene layer by simply growing it on an appropriate substrate. Our comprehensive study demonstrates that, in strongly interacting substrates like Rh(111), graphene adopts highly rippled structures that exhibit areas with distinctive chemical behaviors. According to the local coupling with the substrate, we find areas with markedly different adsorption, dissociation and diffusion pathways for both molecular and atomic oxygen, including a significant change in the nature of the adsorbed molecular and dissociated states, and a dramatic reduction (∼60%) of the O2dissociation energy barrier with respect to free-standing graphene. Our results show that the graphene-metal interaction represents an additional and powerful handle to tailor the graphene chemical properties with potential applications to nano patterning, graphene functionalization and sensing devicesWe thank the financial support from the Spanish MINECO (projects MAT2014-54484-P, MDM-2014-0377, MAT2016-77852-C2-2-R (AEI/FEDER, UE) and MAT2017-83273-R (AEI/FEDER,UE)). Computer time provided by the Spanish Supercomputer Network (RES) at the Magerit (CesViMa, Madrid) and Altamira (IFCA, Santander) supercomputers. CRM is grateful to the FPI-UAM graduate scholarship program and to Fundación Universia for financial suppor

Psychosocial predictors of anxiety in nursing homes staff

Objectives: Although research shows that nursing home staff experience significant levels of stress and burnout, studies analyzing the relationship of psychosocial variables on their feelings of anxiety are scarce. This study aims to analyze the relationship between psychosocial variables and levels of anxiety among staff. Method: Participants were 101 nursing home professionals. In addition to anxiety, socio-demographic variables, depersonalization, burden, relationship with families of the residents, and guilt about the care offered to the residents were assessed. A hierarchical regression analysis was carried out to analyze the contribution of the assessed variables to staff anxiety levels. Results: The obtained model explained 57% of the variance in anxious symptomatology. Guilt about the care offered and poor quality of the relationship with residents’ family were associated with anxiety. Further, working at nursing homes where the staff report higher levels of anxiety symptoms, the presence of depersonalization and burden were also associated with anxiety. Conclusion: The findings suggest that in addition to work-related variables (burden and burnout), problems with family members and guilt about the care offered are relevant variables for understanding staff’s anxious symptomatology. Clinical Implications: Interventions that address issues of guilt about the quality of care, and problematic relationships with family members of residents, may have potential to reduce staff anxiety and promote their well-bein