Towards to a Predictive Model of Academic Performance Using Data Mining in the UTN-FRRe

Students completing the courses required to become an Engineer in Information Systems in the Resistencia Regional Faculty, National Technological University, Argentine (UTN-FRRe), face the challenge of attending classes and fulfilling course regularization requirements, often for correlative course...

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Autores principales: La Red Martínez, David Luis, Karanik, Marcelo, Giovaninni, Mirta Eve, Scappini, Reinaldo
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: 2016
Materias:
academic performance
data warehouses
data mining
predictive models
Acceso en línea:http://hdl.handle.net/20.500.12272/1031
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-1031
record_format dspace
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
topic academic performance
data warehouses
data mining
predictive models
spellingShingle academic performance
data warehouses
data mining
predictive models
La Red Martínez, David Luis
Karanik, Marcelo
Giovaninni, Mirta Eve
Scappini, Reinaldo
Towards to a Predictive Model of Academic Performance Using Data Mining in the UTN-FRRe
topic_facet academic performance
data warehouses
data mining
predictive models
description Students completing the courses required to become an Engineer in Information Systems in the Resistencia Regional Faculty, National Technological University, Argentine (UTN-FRRe), face the challenge of attending classes and fulfilling course regularization requirements, often for correlative courses. Such is the case of freshmen's course Algorithms and Data Structures: it must be regularized in order to be able to attend several second and third year courses. Based on the results of the project entitled “Profiling of students and academic performance through the use of data mining”, 25/L059 - UTI1719, implemented in the aforementioned course (in 2013-2015), a new project has started, aimed to take the descriptive analysis (what happened) as a starting point, and use advanced analytics, trying to explain the why, the what will happen, and how we can address it. Different data mining tools will be used for the study: clustering, neural networks, Bayesian networks, decision trees, regression and time series, etc. These tools allow different
format Artículo
publishedVersion
Artículo
author La Red Martínez, David Luis
Karanik, Marcelo
Giovaninni, Mirta Eve
Scappini, Reinaldo
author_facet La Red Martínez, David Luis
Karanik, Marcelo
Giovaninni, Mirta Eve
Scappini, Reinaldo
author_sort La Red Martínez, David Luis
title Towards to a Predictive Model of Academic Performance Using Data Mining in the UTN-FRRe
title_short Towards to a Predictive Model of Academic Performance Using Data Mining in the UTN-FRRe
title_full Towards to a Predictive Model of Academic Performance Using Data Mining in the UTN-FRRe
title_fullStr Towards to a Predictive Model of Academic Performance Using Data Mining in the UTN-FRRe
title_full_unstemmed Towards to a Predictive Model of Academic Performance Using Data Mining in the UTN-FRRe
title_sort towards to a predictive model of academic performance using data mining in the utn-frre
publishDate 2016
url http://hdl.handle.net/20.500.12272/1031
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