ABSTRACT
The fast-paced evolution of technology has compelled the digitalization of education, requiring educators to interact with computers and develop digital competencies relevant to the teaching-learning process. This need has prompted various organizations to define frameworks for assessing digital competency emphasizing teachers' interaction with computer technologies in education. Different authors have presented assessment methods for teachers' digital competence based on the video analysis of recorded classes using sensors such as cameras, microphones, or electroencephalograms. The main limitation of these solutions is the large number of resources they require, making it difficult to assess large numbers of teachers in resource-constrained environments. This article proposes the automation of teachers' digital competence evaluation process based on monitoring metrics obtained from teachers' interaction with a Learning Management System (LMS). Based on the Digital Competence Framework for Educators (DigCompEdu), indicators were defined and extracted that allow automatic measurement of a teacher's competency level. A tool was designed and implemented to conduct a successful proof of concept capable of automating the evaluation process of all university faculty, including 987 lecturers from different fields of knowledge. Results obtained allow for drawing conclusions on technological adoption according to the teacher's profile and planning educational actions to improve these competencies.
ABSTRACT
Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html .
ABSTRACT
We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply Maximum Satisfiability technology to compute Minimum Pure DTs (MPDTs). We improve the runtime of previous approaches and, show that these MPDTs can outperform the accuracy of DTs generated with the ML framework sklearn.
ABSTRACT
Introducción: las prácticas óptimas de alimentación del lactante y el niño pequeño se sitúan entre las intervenciones con mayor efectividad para mejorar la salud de la niñez. Las encuestas de consumo de alimentos revisten gran importancia en la detección precoz de un consumo inadecuado de alimentos y sus repercusiones sobre la salud. Objetivos: identificar las prácticas reales de alimentación complementaria, y determinar la adecuación de la ingesta de energía y nutrientes. Métodos: se realizó un estudio transversal en niños con edades entre 6 y 23 meses. Comprendió un muestreo aleatorio bietápico. Se calculó la muestra en 560 niños. Se obtuvo información sociodemográfica del niño y su medio familiar. Se aplicó una encuesta de consumo de alimentos por recordatorio de 24 horas. Resultados: se encuestaron 543 madres de niños entre 6 y 23 meses de edad. El 42,4 por ciento de los menores de un año son amamantados con una disminución hasta el 20,9 por ciento durante el segundo año de vida, existe bajo consumo de frutas y vegetales, los menores de 1 año de edad consumieron como promedio 914 kcal, el 45,3 por ciento sobrepasa las recomendaciones de energía para esas edades y presenta un consumo excesivo, mientras los de 1 a 2 años consumen 1 052 kcal. Respecto a los minerales, se destaca el bajo consumo de hierro, con 51,1 por ciento de niños con un consumo deficitario de este mineral. Conclusiones: el elevado porcentaje de niños con ingestas excesivas de energía constituye un factor predisponente a la obesidad desde las etapas tempranas de la vida(AU)
Introduction: optimal infant and young child feeding practices are among the most effective interventions to improve child health. Food consumption surveys are extremely important for early detection of inadequate food intake and its impact on health. Objectives: identify actual complementary feeding practices and determine the adequacy of energy and nutrient intake. Methods: a cross-sectional study was conducted with children aged 6-23 months. The study included two-staged random sampling. The sample was composed of 560 children. Sociodemographic information was obtained about the children and their family environment. A food intake survey was applied based on 24-hour recall. Results: the survey was applied to 543 mothers of children aged 6-23 months. It was found that 42.4 percent of the children under one year of age are breastfed, a percentage that falls to 20.9 percent during the second year of life. Fruit and vegetable intake is low. Children under one year of age consumed 914 kcal on average, and 45.3 percent exceed the energy intake recommendations for that age, which constituted excessive consumption. Children aged 1-2 years consume 1 052 kcal. As to minerals, low iron intake stands out, with 51.1 percent of the children consuming less than required. Conclusions: the high percentage of children with an excessive energy intake is a risk factor for obesity since early life stages(AU)
Subject(s)
Humans , Male , Female , Infant , Child Nutrition/education , Eating/standards , Breast Feeding/methods , Energy Intake , Retrospective StudiesABSTRACT
Introducción: las prácticas óptimas de alimentación del lactante y el niño pequeño se sitúan entre las intervenciones con mayor efectividad para mejorar la salud de la niñez. Las encuestas de consumo de alimentos revisten gran importancia en la detección precoz de un consumo inadecuado de alimentos y sus repercusiones sobre la salud. Objetivos: identificar las prácticas reales de alimentación complementaria, y determinar la adecuación de la ingesta de energía y nutrientes. Métodos: se realizó un estudio transversal en niños con edades entre 6 y 23 meses. Comprendió un muestreo aleatorio bietápico. Se calculó la muestra en 560 niños. Se obtuvo información sociodemográfica del niño y su medio familiar. Se aplicó una encuesta de consumo de alimentos por recordatorio de 24 horas. Resultados: se encuestaron 543 madres de niños entre 6 y 23 meses de edad. El 42,4 por ciento de los menores de un año son amamantados con una disminución hasta el 20,9 por ciento durante el segundo año de vida, existe bajo consumo de frutas y vegetales, los menores de 1 año de edad consumieron como promedio 914 kcal, el 45,3 por ciento sobrepasa las recomendaciones de energía para esas edades y presenta un consumo excesivo, mientras los de 1 a 2 años consumen 1 052 kcal. Respecto a los minerales, se destaca el bajo consumo de hierro, con 51,1 por ciento de niños con un consumo deficitario de este mineral. Conclusiones: el elevado porcentaje de niños con ingestas excesivas de energía constituye un factor predisponente a la obesidad desde las etapas tempranas de la vida(AU)
Introduction: optimal infant and young child feeding practices are among the most effective interventions to improve child health. Food consumption surveys are extremely important for early detection of inadequate food intake and its impact on health. Objectives: identify actual complementary feeding practices and determine the adequacy of energy and nutrient intake. Methods: a cross-sectional study was conducted with children aged 6-23 months. The study included two-staged random sampling. The sample was composed of 560 children. Sociodemographic information was obtained about the children and their family environment. A food intake survey was applied based on 24-hour recall. Results: the survey was applied to 543 mothers of children aged 6-23 months. It was found that 42.4 percent of the children under one year of age are breastfed, a percentage that falls to 20.9 percent during the second year of life. Fruit and vegetable intake is low. Children under one year of age consumed 914 kcal on average, and 45.3 percent exceed the energy intake recommendations for that age, which constituted excessive consumption. Children aged 1-2 years consume 1 052 kcal. As to minerals, low iron intake stands out, with 51.1 percent of the children consuming less than required. Conclusions: the high percentage of children with an excessive energy intake is a risk factor for obesity since early life stages(AU)
