Adaptive Model of Classification of Professions in Vocational Guidance Systems
Vocational guidance is part of psychosocial development and is understood as a method that helps to determine the most appropriate profession according to the aptitudes and abilities of the student. The processes of vocational guidance are dynamic and focus on educating and favoring the decision-mak...
| Main Authors: | , |
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| Format: | Online |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2022
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14841 |
| _version_ | 1801706101061713920 |
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| author | Cruz-Eraso, Andrés-Felipe González-Serrano, Carolina |
| author_facet | Cruz-Eraso, Andrés-Felipe González-Serrano, Carolina |
| author_sort | Cruz-Eraso, Andrés-Felipe |
| collection | OJS |
| description | Vocational guidance is part of psychosocial development and is understood as a method that helps to determine the most appropriate profession according to the aptitudes and abilities of the student. The processes of vocational guidance are dynamic and focus on educating and favoring the decision-making process in the professional choice for a learning pathway throughout the student's life, which will benefit society in the long run. Most of the current solutions, both theoretical and applied, from Europe and North America differ when used in the Colombian context, mainly for adults, since the process of classifying professions is not accurate nor precise. In addition, there are various educational projects and evaluation systems in secondary education level institutions. At this level, the students have a changing vocational choice which implies taking into account specific characteristics of the context, also, the student profile vocational guidance determinants. The objective of this article is to describe the adaptive model of occupational classification integrated into the Intelligent Web Platform used in educational institutions in the Department of Cauca. The use of the CRISP-DM methodology allowed finding the Naive Bayes and Deep learning algorithms as those with the best performance in the classification of professions. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-14841 |
| institution | Revista Facultad de Ingeniería |
| language | eng |
| publishDate | 2022 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-148412022-11-18T19:23:23Z Adaptive Model of Classification of Professions in Vocational Guidance Systems Modelo adaptativo de clasificación de profesiones en sistemas de orientación vocacional Cruz-Eraso, Andrés-Felipe González-Serrano, Carolina adaptive intelligent datamining vocational guidance web platform inteligencia adaptativa minería de datos orientación vocacional plataforma web Vocational guidance is part of psychosocial development and is understood as a method that helps to determine the most appropriate profession according to the aptitudes and abilities of the student. The processes of vocational guidance are dynamic and focus on educating and favoring the decision-making process in the professional choice for a learning pathway throughout the student's life, which will benefit society in the long run. Most of the current solutions, both theoretical and applied, from Europe and North America differ when used in the Colombian context, mainly for adults, since the process of classifying professions is not accurate nor precise. In addition, there are various educational projects and evaluation systems in secondary education level institutions. At this level, the students have a changing vocational choice which implies taking into account specific characteristics of the context, also, the student profile vocational guidance determinants. The objective of this article is to describe the adaptive model of occupational classification integrated into the Intelligent Web Platform used in educational institutions in the Department of Cauca. The use of the CRISP-DM methodology allowed finding the Naive Bayes and Deep learning algorithms as those with the best performance in the classification of professions. La orientación vocacional forma parte del desarrollo psicosocial y se entiende, como un método que ayuda a determinar la profesión más adecuada, en función de las aptitudes y capacidades de los estudiantes. Los procesos de orientación vocacional, son dinámicos y se enfocan en educar y favorecer el proceso de toma de decisiones en la elección profesional. proporcionándoles un camino de aprendizaje claro para seguir a lo largo de su vida y para que la sociedad se beneficie de su talento enfocado. La mayoría de soluciones actuales, teóricas y de aplicación, de Europa y Norteamérica, difieren al utilizarse en el contexto colombiano, principalmente para adultos, las cuales en términos del proceso de clasificación de profesiones presentan bajos niveles de exactitud y precisión. Además, existen diversos proyectos educativos y sistemas de evaluación en los centros de enseñanza secundaria. En este nivel de educación, los estudiantes tienen una decisión vocacional cambiante que implica tener en cuenta características específicas del contexto, también, los determinantes su perfil de orientación vocacional. El objetivo del presente artículo es describir el modelo adaptativo de clasificación de profesiones, integrado a la Plataforma Web Inteligente, utilizada en instituciones educativas del Departamento del Cauca. El uso de la metodología CRISP-DM permitió encontrar los algoritmos de Naive Bayes y Deep learning como los de mejor desempeño en la clasificación de profesiones. Universidad Pedagógica y Tecnológica de Colombia 2022-09-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14841 10.19053/01211129.v31.n61.2022.14841 Revista Facultad de Ingeniería; Vol. 31 No. 61 (2022): July-September 2022 (Continuous Publication); e14841 Revista Facultad de Ingeniería; Vol. 31 Núm. 61 (2022): Julio-Septiembre 2022 (Publicación Continua); e14841 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14841/12273 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14841/12321 Copyright (c) 2022 Andr´és-Felipe Crus-Eraso, Carolina González-Serrano http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | adaptive intelligent datamining vocational guidance web platform inteligencia adaptativa minería de datos orientación vocacional plataforma web Cruz-Eraso, Andrés-Felipe González-Serrano, Carolina Adaptive Model of Classification of Professions in Vocational Guidance Systems |
| title | Adaptive Model of Classification of Professions in Vocational Guidance Systems |
| title_alt | Modelo adaptativo de clasificación de profesiones en sistemas de orientación vocacional |
| title_full | Adaptive Model of Classification of Professions in Vocational Guidance Systems |
| title_fullStr | Adaptive Model of Classification of Professions in Vocational Guidance Systems |
| title_full_unstemmed | Adaptive Model of Classification of Professions in Vocational Guidance Systems |
| title_short | Adaptive Model of Classification of Professions in Vocational Guidance Systems |
| title_sort | adaptive model of classification of professions in vocational guidance systems |
| topic | adaptive intelligent datamining vocational guidance web platform inteligencia adaptativa minería de datos orientación vocacional plataforma web |
| topic_facet | adaptive intelligent datamining vocational guidance web platform inteligencia adaptativa minería de datos orientación vocacional plataforma web |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14841 |
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