Studying academic success: A data analytics approach to predict performance of higher education students

The dropout of students in higher education is a concern for universities, as it directly impacts the community and the educational level of future generations. For this reason, a data analytics-based model is proposed to support students in making decisions during the course selection process, aimi...

Full description

Autores:
Martínez Osorio, Daniel Felipe
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2024
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/73243
Acceso en línea:
https://hdl.handle.net/1992/73243
Palabra clave:
Performance
Data analytics
Ingeniería
Rights
openAccess
License
Attribution 4.0 International
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dc.title.eng.fl_str_mv Studying academic success: A data analytics approach to predict performance of higher education students
title Studying academic success: A data analytics approach to predict performance of higher education students
spellingShingle Studying academic success: A data analytics approach to predict performance of higher education students
Performance
Data analytics
Ingeniería
title_short Studying academic success: A data analytics approach to predict performance of higher education students
title_full Studying academic success: A data analytics approach to predict performance of higher education students
title_fullStr Studying academic success: A data analytics approach to predict performance of higher education students
title_full_unstemmed Studying academic success: A data analytics approach to predict performance of higher education students
title_sort Studying academic success: A data analytics approach to predict performance of higher education students
dc.creator.fl_str_mv Martínez Osorio, Daniel Felipe
dc.contributor.advisor.none.fl_str_mv Manrique Piramanrique, Rubén Francisco
Benítez Amaya, Andrés Felipe
dc.contributor.author.none.fl_str_mv Martínez Osorio, Daniel Felipe
dc.contributor.researchgroup.none.fl_str_mv Facultad de Ingeniería
dc.subject.keyword.eng.fl_str_mv Performance
Data analytics
topic Performance
Data analytics
Ingeniería
dc.subject.themes.spa.fl_str_mv Ingeniería
description The dropout of students in higher education is a concern for universities, as it directly impacts the community and the educational level of future generations. For this reason, a data analytics-based model is proposed to support students in making decisions during the course selection process, aiming to guide them towards completing their degree while maximizing their performance. We have a dataset for three different majors in the Universidad de los Andes, (Systems and Computer Engineering, Industrial Engineering, and Economics), containing historical information about students, the courses they chose each semester in their specific curriculum, and their grades. Based on this data, the model analyzes the completed courses and the ones remaining for each student to fulfill their curriculum requirements. In this way, it creates a student profile that is used to calculate the probability of achieving certain grades in their next semester. Assuming this result, the process is iterated to develop a curriculum plan for the upcoming semesters. This outcome will provide students with a course guide for each semester, increasing their likelihood of achieving better performance in their studies.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-01-15T19:40:54Z
dc.date.available.none.fl_str_mv 2024-01-15T19:40:54Z
dc.date.issued.none.fl_str_mv 2024-01-10
dc.type.none.fl_str_mv Trabajo de grado - Pregrado
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
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dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/1992/73243
dc.identifier.instname.none.fl_str_mv instname:Universidad de los Andes
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url https://hdl.handle.net/1992/73243
identifier_str_mv instname:Universidad de los Andes
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dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.en.fl_str_mv Attribution 4.0 International
dc.rights.uri.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
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eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 28 páginas
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad de los Andes
dc.publisher.program.none.fl_str_mv Ingeniería de Sistemas y Computación
dc.publisher.faculty.none.fl_str_mv Facultad de Ingeniería
dc.publisher.department.none.fl_str_mv Departamento de Ingeniería Sistemas y Computación
publisher.none.fl_str_mv Universidad de los Andes
institution Universidad de los Andes
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spelling Manrique Piramanrique, Rubén FranciscoBenítez Amaya, Andrés FelipeMartínez Osorio, Daniel FelipeFacultad de Ingeniería2024-01-15T19:40:54Z2024-01-15T19:40:54Z2024-01-10https://hdl.handle.net/1992/73243instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The dropout of students in higher education is a concern for universities, as it directly impacts the community and the educational level of future generations. For this reason, a data analytics-based model is proposed to support students in making decisions during the course selection process, aiming to guide them towards completing their degree while maximizing their performance. We have a dataset for three different majors in the Universidad de los Andes, (Systems and Computer Engineering, Industrial Engineering, and Economics), containing historical information about students, the courses they chose each semester in their specific curriculum, and their grades. Based on this data, the model analyzes the completed courses and the ones remaining for each student to fulfill their curriculum requirements. In this way, it creates a student profile that is used to calculate the probability of achieving certain grades in their next semester. Assuming this result, the process is iterated to develop a curriculum plan for the upcoming semesters. 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