Modeling Students through Analysis of Social Networks Topics

Autores
Charnelli, María Emilia; Lanzarini, Laura Cristina; Díaz, Francisco Javier
Año de publicación
2016
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Educational Data Mining gathers the multiple methods that allow new and useful information extraction from great volumes of data coming from the educational context. The goal of this article is to obtain a model of the students of the Computer Science School of the UNLP from their participation in Facebook. The work describes the process of extraction of latent topics in posts made in public groups related to the School, and the modeling of the students from the topics discovered. Additionally, it includes the preprocessing done to the collected data, which constitutes a fundamental stage since it strongly conditions the performance of the models to be obtained. Finally, obtained results are presented together with conclusions and future lines of work.
XIII Workshop Tecnología Informática Aplicada en Educación (WTIAE).
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Minería de Datos
learning analytics
topic modeling
user modeling
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/55814
Acceder
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network_name_str SEDICI (UNLP)
spelling Modeling Students through Analysis of Social Networks TopicsCharnelli, María EmiliaLanzarini, Laura CristinaDíaz, Francisco JavierCiencias InformáticasMinería de Datoslearning analyticstopic modelinguser modelingEducational Data Mining gathers the multiple methods that allow new and useful information extraction from great volumes of data coming from the educational context. The goal of this article is to obtain a model of the students of the Computer Science School of the UNLP from their participation in Facebook. The work describes the process of extraction of latent topics in posts made in public groups related to the School, and the modeling of the students from the topics discovered. Additionally, it includes the preprocessing done to the collected data, which constitutes a fundamental stage since it strongly conditions the performance of the models to be obtained. Finally, obtained results are presented together with conclusions and future lines of work.XIII Workshop Tecnología Informática Aplicada en Educación (WTIAE).Red de Universidades con Carreras en Informática (RedUNCI)2016-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf363-371http://sedici.unlp.edu.ar/handle/10915/55814enginfo:eu-repo/semantics/reference/hdl/10915/55718info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-17T09:49:14Zoai:sedici.unlp.edu.ar:10915/55814Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-17 09:49:14.903SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Modeling Students through Analysis of Social Networks Topics
title Modeling Students through Analysis of Social Networks Topics
spellingShingle Modeling Students through Analysis of Social Networks Topics
Charnelli, María Emilia
Ciencias Informáticas
Minería de Datos
learning analytics
topic modeling
user modeling
title_short Modeling Students through Analysis of Social Networks Topics
title_full Modeling Students through Analysis of Social Networks Topics
title_fullStr Modeling Students through Analysis of Social Networks Topics
title_full_unstemmed Modeling Students through Analysis of Social Networks Topics
title_sort Modeling Students through Analysis of Social Networks Topics
dc.creator.none.fl_str_mv Charnelli, María Emilia
Lanzarini, Laura Cristina
Díaz, Francisco Javier
author Charnelli, María Emilia
author_facet Charnelli, María Emilia
Lanzarini, Laura Cristina
Díaz, Francisco Javier
author_role author
author2 Lanzarini, Laura Cristina
Díaz, Francisco Javier
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Minería de Datos
learning analytics
topic modeling
user modeling
topic Ciencias Informáticas
Minería de Datos
learning analytics
topic modeling
user modeling
dc.description.none.fl_txt_mv Educational Data Mining gathers the multiple methods that allow new and useful information extraction from great volumes of data coming from the educational context. The goal of this article is to obtain a model of the students of the Computer Science School of the UNLP from their participation in Facebook. The work describes the process of extraction of latent topics in posts made in public groups related to the School, and the modeling of the students from the topics discovered. Additionally, it includes the preprocessing done to the collected data, which constitutes a fundamental stage since it strongly conditions the performance of the models to be obtained. Finally, obtained results are presented together with conclusions and future lines of work.
XIII Workshop Tecnología Informática Aplicada en Educación (WTIAE).
Red de Universidades con Carreras en Informática (RedUNCI)
description Educational Data Mining gathers the multiple methods that allow new and useful information extraction from great volumes of data coming from the educational context. The goal of this article is to obtain a model of the students of the Computer Science School of the UNLP from their participation in Facebook. The work describes the process of extraction of latent topics in posts made in public groups related to the School, and the modeling of the students from the topics discovered. Additionally, it includes the preprocessing done to the collected data, which constitutes a fundamental stage since it strongly conditions the performance of the models to be obtained. Finally, obtained results are presented together with conclusions and future lines of work.
publishDate 2016
dc.date.none.fl_str_mv 2016-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
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