Optimizando el diseño instruccional mediante algoritmos genéticos
| dc.contributor.advisor | Chicaiza Becerra, Liliana Alejandra | |
| dc.contributor.author | Cárdenas Benavides, Erick Sebastián | |
| dc.date.accessioned | 2024-10-31T14:05:58Z | |
| dc.date.available | 2024-10-31T14:05:58Z | |
| dc.date.issued | 2024 | |
| dc.description | ilustraciones, diagramas, tablas | spa |
| dc.description.abstract | El entrenamiento de empleados es crucial, pero las dificultades en el diseño instruccional (DI) reducen su efectividad. Estas dificultades incluyen la falta de evidencia empírica, la dependencia en expertos y sesgos. El DI como problema de optimizacion busca maximizar el aprendizaje y minimizar costos. Los algoritmos genéticos pueden resolver este problema. El objetivo es crear un algoritmo genético que compita con expertos en diseño instruccional. Usando una metodología descriptiva comparativa, se evaluaron diseños de expertos y un algoritmo genetico.Resultados muestran que el algoritmo crea entrenamientos 50.3% más económicos que expertos y diseños superiores en 97.8% de casos. Concluyendo, la implementación de algoritmos genéticos para diseños instruccionales no solo es factible, sino que ofrece beneficios significativos. (Texto tomado de la fuente) | spa |
| dc.description.abstract | Employee training is crucial, but difficulties in instructional design can reduce its effectiveness. These problems include a lack of empirical evidence, dependence on experts, and biases. Instructional design as an optimization selection problem aims to maximize learning and minimize costs. Genetic algorithms can help solve this problem. The goal is to create a genetic algorithm that can compete with experts in instructional design. Using a comparative descriptive methodology, expert designs and the GTD algorithm were evaluated. 9 training cases were analyzed, evaluated by 5 experts and the algorithm. The results show that the algorithm creates training that is 50.3% more economical than experts and designs that are superior in 97.8% of cases. In conclusion, the implementation of genetic algorithms for instructional designs is not only feasible, but it also offers significant benefits. | eng |
| dc.description.degreelevel | Maestría | spa |
| dc.description.degreename | Magíster en Administración | spa |
| dc.format.extent | 101 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.instname | Universidad Nacional de Colombia | spa |
| dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
| dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/87123 | |
| dc.language.iso | spa | spa |
| dc.publisher | Universidad Nacional de Colombia | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
| dc.publisher.faculty | Facultad de Ciencias Económicas | spa |
| dc.publisher.place | Bogotá, Colombia | spa |
| dc.publisher.program | Bogotá - Ciencias Económicas - Maestría en Administración | spa |
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| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial 4.0 Internacional | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | spa |
| dc.subject.ddc | 020 - Bibliotecología y ciencias de la información::023 - Administración de personal (Administración de recurso humano) | spa |
| dc.subject.proposal | Entrenamiento | spa |
| dc.subject.proposal | Optimización | spa |
| dc.subject.proposal | Algoritmo genetico | spa |
| dc.subject.proposal | Diseño instruccional | spa |
| dc.subject.proposal | Training | eng |
| dc.subject.proposal | Optimization | eng |
| dc.subject.proposal | Genetic algorithms | eng |
| dc.subject.proposal | Instructional design | eng |
| dc.subject.proposal | Genético | spa |
| dc.subject.proposal | Optimization | eng |
| dc.subject.proposal | Algorithm | eng |
| dc.subject.proposal | Genetic | eng |
| dc.subject.wikidata | genetic algorithm | eng |
| dc.subject.wikidata | algoritmo genético | spa |
| dc.subject.wikidata | instructional design | eng |
| dc.subject.wikidata | Diseño instruccional | spa |
| dc.subject.wikidata | capacity building | eng |
| dc.subject.wikidata | desarrollo de la capacidad | spa |
| dc.title | Optimizando el diseño instruccional mediante algoritmos genéticos | spa |
| dc.title.translated | Optimizing instructional design through genetic algorithms | eng |
| dc.type | Trabajo de grado - Maestría | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/TM | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| dcterms.audience.professionaldevelopment | Investigadores | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
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