Metodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distancia

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dc.contributor.advisorVelásquez-Henao, Juan David
dc.contributor.advisorFranco Cardona, Carlos Jaime
dc.contributor.authorGil Vera, Victor Daniel
dc.contributor.researchgroupBig Data y Data Analyticsspa
dc.date.accessioned2022-03-02T16:37:05Z
dc.date.available2022-03-02T16:37:05Z
dc.date.issued2021-11
dc.descriptionilustraciones, diagramas, tablasspa
dc.description.abstractEl 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)spa
dc.description.abstractThe 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.eng
dc.description.curricularareaÁrea Curricular de Ingeniería de Sistemas e Informáticaspa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.description.researchareaAnalíticaspa
dc.format.extentxix, 124 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/81113
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.departmentDepartamento de la Computación y la Decisiónspa
dc.publisher.facultyFacultad de Minasspa
dc.publisher.placeMedellín, Colombiaspa
dc.publisher.programMedellín - Minas - Doctorado en Ingeniería - Sistemasspa
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dc.subject.proposalMachine Learningeng
dc.subject.proposalPredicción
dc.subject.proposalRendimiento
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dc.subject.proposalStudenteng
dc.subject.proposalMachine Learningeng
dc.subject.proposalPerformanceeng
dc.subject.proposalPredictioneng
dc.titleMetodología para predecir el desempeño estudiantil en cursos universitarios virtuales a distanciaspa
dc.title.translatedMethodology for predicting student performance in virtual university distance learning courseseng
dc.typeTrabajo de grado - Doctoradospa
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