Predicting academic success through students’ interaction with Version Control Systems

[EN] Version Control Systems are commonly used by Information and communication technology professionals. These systems allow monitoring programmers activity working in a project. Thus, Version Control Systems are also used by educational institutions. The aim of thiswork is to evaluate if the acade...

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Detalles Bibliográficos
Autores: Guerrero Higueras, Ángel Manuel, Castro García, Noemí de, Rodríguez Lera, Francisco Javier, Matellán Olivera, Vicente, Conde González, Miguel Ángel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/19088
Acceso en línea:https://hdl.handle.net/10612/19088
Access Level:acceso abierto
Palabra clave:Cibernética
Educación
Version Control System
Machine learning
Learning analytics
1207.03 Cibernética
1203.10 Enseñanza Con Ayuda de Ordenador
id ES_44db109fa48eeef064fa2e17afbfbdb4
oai_identifier_str oai:buleria.unileon.es:10612/19088
network_acronym_str ES
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repository_id_str
spelling Predicting academic success through students’ interaction with Version Control SystemsGuerrero Higueras, Ángel ManuelCastro García, Noemí deRodríguez Lera, Francisco JavierMatellán Olivera, VicenteConde González, Miguel ÁngelCibernéticaEducaciónVersion Control SystemMachine learningLearning analytics1207.03 Cibernética1203.10 Enseñanza Con Ayuda de Ordenador[EN] Version Control Systems are commonly used by Information and communication technology professionals. These systems allow monitoring programmers activity working in a project. Thus, Version Control Systems are also used by educational institutions. The aim of thiswork is to evaluate if the academic success of students may be predicted by monitoring their interaction with a Version Control System. In order to do so, we have built a Machine Learning modelwhich predicts student results in a specific practical assignment of the Operating Systems Extension subject, from the second course of the degree in Computer Science of the University of León, through their interaction with a Git repository. To build the model, several classifiers and predictors have been evaluated. In order to do so, we have developed Model Evaluator (MoEv), a tool to evaluate Machine Learning models in order to get the most suitable for a specific problem. Prior to the model development, a feature selection from input data is done. The resulting model has been trained using results from 2016– 2017 course and later validated using results from 2017– 2018 course. Results conclude that the model predicts students’ success with a success high percentage.SIDe GruyterArquitectura y Tecnologia de ComputadoresEscuela de Ingenierias Industrial, Informática y Aeroespacial2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://hdl.handle.net/10612/19088reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/190882026-06-24T12:43:27Z
dc.title.none.fl_str_mv Predicting academic success through students’ interaction with Version Control Systems
title Predicting academic success through students’ interaction with Version Control Systems
spellingShingle Predicting academic success through students’ interaction with Version Control Systems
Guerrero Higueras, Ángel Manuel
Cibernética
Educación
Version Control System
Machine learning
Learning analytics
1207.03 Cibernética
1203.10 Enseñanza Con Ayuda de Ordenador
title_short Predicting academic success through students’ interaction with Version Control Systems
title_full Predicting academic success through students’ interaction with Version Control Systems
title_fullStr Predicting academic success through students’ interaction with Version Control Systems
title_full_unstemmed Predicting academic success through students’ interaction with Version Control Systems
title_sort Predicting academic success through students’ interaction with Version Control Systems
dc.creator.none.fl_str_mv Guerrero Higueras, Ángel Manuel
Castro García, Noemí de
Rodríguez Lera, Francisco Javier
Matellán Olivera, Vicente
Conde González, Miguel Ángel
author Guerrero Higueras, Ángel Manuel
author_facet Guerrero Higueras, Ángel Manuel
Castro García, Noemí de
Rodríguez Lera, Francisco Javier
Matellán Olivera, Vicente
Conde González, Miguel Ángel
author_role author
author2 Castro García, Noemí de
Rodríguez Lera, Francisco Javier
Matellán Olivera, Vicente
Conde González, Miguel Ángel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnologia de Computadores
Escuela de Ingenierias Industrial, Informática y Aeroespacial
dc.subject.none.fl_str_mv Cibernética
Educación
Version Control System
Machine learning
Learning analytics
1207.03 Cibernética
1203.10 Enseñanza Con Ayuda de Ordenador
topic Cibernética
Educación
Version Control System
Machine learning
Learning analytics
1207.03 Cibernética
1203.10 Enseñanza Con Ayuda de Ordenador
description [EN] Version Control Systems are commonly used by Information and communication technology professionals. These systems allow monitoring programmers activity working in a project. Thus, Version Control Systems are also used by educational institutions. The aim of thiswork is to evaluate if the academic success of students may be predicted by monitoring their interaction with a Version Control System. In order to do so, we have built a Machine Learning modelwhich predicts student results in a specific practical assignment of the Operating Systems Extension subject, from the second course of the degree in Computer Science of the University of León, through their interaction with a Git repository. To build the model, several classifiers and predictors have been evaluated. In order to do so, we have developed Model Evaluator (MoEv), a tool to evaluate Machine Learning models in order to get the most suitable for a specific problem. Prior to the model development, a feature selection from input data is done. The resulting model has been trained using results from 2016– 2017 course and later validated using results from 2017– 2018 course. Results conclude that the model predicts students’ success with a success high percentage.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10612/19088
url https://hdl.handle.net/10612/19088
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv De Gruyter
publisher.none.fl_str_mv De Gruyter
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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