Resumen En este artículo se analizan los modelos institucionales de competencia digital docente con mayor impacto en los últimos tiempos y se abordan críticamente tres características comunes a todos ellos, entendidas como deficiencias en su planteamiento. En primer lugar, que no parten explícitamente de un modelo de acción docente y de manera implícita reducen la función docente al trabajo en el aula, evitando aspectos como el compromiso social y político o el papel de la escuela en el desarrollo comunitario. En segundo lugar, que parten de una visión taxonómica del concepto de competencia, ignorando la complejidad de los diferentes contextos en los que dicha competencia se pone en acción y se desarrolla y en su papel en la construcción de la identidad del docente, algo que enfoques sociomateriales y holísticos sí incluyen. En tercer lugar, que se fundamentan de manera contradictoria en una visión instrumentalista de la tecnología como herramienta neutra en valores y, por el contrario, en una concepción determinista de la relación entre tecnología y sociedad. Finalmente, se propone a debate un modelo que hemos denominado "Competencia Docente Integral en el mundo digital", específicamente diseñado para la educación básica, que está siendo objeto de una investigación más ambiciosa Palabras clave Competencia docente, competencia digital docente, acción docente, competencia holística, desarrollo profesional docente.
Machine Learning classification models have been trained and validated from a dataset (73 features and 13,616 instances) including experimental information of a clean cold forming steel fabricated by electric arc furnace and hot rolling. A classification model was developed to identify inclusion contents above the median. The following algorithms were implemented: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forests, AdaBoost, Gradient Boosting, Support Vector Classifier and Artificial Neural Networks. Random Forest displayed the best results overall and was selected for the subsequent analyses. The Permutation Importance method was used to identify the variables that influence the inclusion cleanliness and the impact of these variables was determined by means of Partial Dependence Plots. The influence of the final diameter of the coil has been interpreted considering the changes induced by the process of hot rolling in the distribution of inclusions. Several variables related to the secondary metallurgy and tundish operations have been identified and interpreted in metallurgical terms. In addition, the inspection area during the microscopic examination of the samples also appears to influence the inclusion content. Recommendations have been established for the sampling process and for the manufacturing conditions to optimize the inclusionary cleanliness of the steel.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.