Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia
ilustraciones, diagramas, tablas
- Autores:
-
Gil Vera, Victor Daniel
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81113
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
370 - Educación::378 - Educación superior (Educación terciaria)
Distance education
Educación a distancia
Universidad a distancia
Educación
Estudiante
Machine Learning
Education
Student
Machine Learning
Performance
Prediction
Predicción
Rendimiento
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia |
| dc.title.translated.eng.fl_str_mv |
Methodology for predicting student performance in virtual university distance learning courses |
| title |
Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia |
| spellingShingle |
Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 370 - Educación::378 - Educación superior (Educación terciaria) Distance education Educación a distancia Universidad a distancia Educación Estudiante Machine Learning Education Student Machine Learning Performance Prediction Predicción Rendimiento |
| title_short |
Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia |
| title_full |
Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia |
| title_fullStr |
Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia |
| title_full_unstemmed |
Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia |
| title_sort |
Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia |
| dc.creator.fl_str_mv |
Gil Vera, Victor Daniel |
| dc.contributor.advisor.none.fl_str_mv |
Velásquez-Henao, Juan David Franco Cardona, Carlos Jaime |
| dc.contributor.author.none.fl_str_mv |
Gil Vera, Victor Daniel |
| dc.contributor.researchgroup.spa.fl_str_mv |
Big Data y Data Analytics |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 370 - Educación::378 - Educación superior (Educación terciaria) |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 370 - Educación::378 - Educación superior (Educación terciaria) Distance education Educación a distancia Universidad a distancia Educación Estudiante Machine Learning Education Student Machine Learning Performance Prediction Predicción Rendimiento |
| dc.subject.lemb.none.fl_str_mv |
Distance education Educación a distancia Universidad a distancia |
| dc.subject.proposal.spa.fl_str_mv |
Educación Estudiante |
| dc.subject.proposal.eng.fl_str_mv |
Machine Learning Education Student Machine Learning Performance Prediction |
| dc.subject.proposal.none.fl_str_mv |
Predicción Rendimiento |
| description |
ilustraciones, diagramas, tablas |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-11 |
| dc.date.accessioned.none.fl_str_mv |
2022-03-02T16:37:05Z |
| dc.date.available.none.fl_str_mv |
2022-03-02T16:37:05Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
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info:eu-repo/semantics/doctoralThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_db06 |
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Text |
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http://purl.org/redcol/resource_type/TD |
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http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/81113 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/81113 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Velásquez-Henao, Juan David17e47270622895774ea0d4811334dd31600Franco Cardona, Carlos Jaimee77c35ea37c7b92041b06767ea4b4d60600Gil Vera, Victor Daniel332d619d94ae55f120b10a7c74f3e137600Big Data y Data Analytics2022-03-02T16:37:05Z2022-03-02T16:37:05Z2021-11https://repositorio.unal.edu.co/handle/unal/81113Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, tablasEl incremento masivo de cursos universitarios virtuales a distancia en universidades e Instituciones de Educación Superior a nivel mundial, ha llevado al incremento en la generación de información relacionada con el rendimiento académico estudiantil; esta información puede ser aprovechada para predecir el desempeño académico y prevenir la mortalidad académica y la deserción. A partir de los resultados de la revisión sistemática de literatura se identificó que no existe una metodología que permita a los docentes de cursos universitarios virtuales a distancia predecir el rendimiento académico estudiantil; algunas investigaciones presentan ejercicios de clasificación sobre el desempeño de los estudiantes; pero no establecen un procedimiento formal que pueda ser empleado por docentes de cualquier área de conocimiento que dicten este tipo de cursos. El principal aporte de esta investigación doctoral es la creación de una metodología para predecir el desempeño académico (Aprueba/Reprueba) en cursos universitarios virtuales a distancia. En resumen, la metodología está conformada por los siguientes pasos; determinación de las variables a analizar, construcción de la base de datos, construcción de los modelos de predicción, evaluación de los modelos y visualización de la predicción. La metodología va más allá del Machine Learning dado que esta considera aspectos relevantes del contexto educativo que deben ser considerados para que las predicciones tengan sentido. Se concluye que la metodología formulada tiene una alta precisión e involucra diferentes aspectos relacionados con la vida académica y personal de los estudiantes, ya que el rendimiento académico estudiantil en este tipo de cursos depende de diversos factores. (Texto tomado de la fuente)The massive increase of virtual university distance learning courses in universities and Higher Education Institutions worldwide has led to an increase in the generation of information related to student academic performance; this information can be used to predict academic performance and prevent academic mortality and dropout. From the results of the systematic literature review, it was identified that there is no methodology that allows teachers of virtual distance university courses to predict student academic performance; some researches present classification exercises on student performance; but they do not establish a formal procedure that can be used by teachers of any area of knowledge who teach this type of courses. The main contribution of this doctoral research is the creation of a methodology to predict academic performance (Pass/Fail) in virtual distance university courses. In summary, the methodology consists of the following steps; determination of the variables to be analyzed, construction of the database, construction of the prediction models, evaluation of the models and visualization of the prediction. The methodology goes beyond Machine Learning since it considers relevant aspects of the educational context that must be considered for the predictions to make sense. This research concludes that the formulated methodology has a high accuracy and involves different aspects related to the academic and personal life of the students, since student academic performance in this type of courses depends on several factors.DoctoradoDoctor en IngenieríaAnalíticaÁrea Curricular de Ingeniería de Sistemas e Informáticaxix, 124 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación370 - Educación::378 - Educación superior (Educación terciaria)Distance educationEducación a distanciaUniversidad a distanciaEducaciónEstudianteMachine LearningEducationStudentMachine LearningPerformancePredictionPredicciónRendimientoMetodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distanciaMethodology for predicting student performance in virtual university distance learning coursesTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAbu Tair, M. 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Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-804535-0.09995-0InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81113/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54ORIGINAL1152186415.2021.pdf1152186415.2021.pdfTesis de Doctorado en Ingeniería - Sistemasapplication/pdf3504123https://repositorio.unal.edu.co/bitstream/unal/81113/3/1152186415.2021.pdf2b83a57575d7cec7c8ee1b7b1e3c017aMD53THUMBNAIL1152186415.2021.pdf.jpg1152186415.2021.pdf.jpgGenerated Thumbnailimage/jpeg5035https://repositorio.unal.edu.co/bitstream/unal/81113/5/1152186415.2021.pdf.jpg593899619d90e48803ae20bc15344303MD55unal/81113oai:repositorio.unal.edu.co:unal/811132023-10-25 11:02:37.903Repositorio Institucional Universidad Nacional de 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