Grupo UTN GIESIN - Difusión Científica - Artículos de Revista

Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/681

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Author: search.filters.author.La Red Martínez, David Luis

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Now showing 1 - 8 of 8
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    Educational data mining : an experience in UTN FRRe
    (2018-03-13) La Red Martínez, David Luis; Giovannini, Mirtha; Báez, María Eugenia; Torre, Juliana; Yaccuzzi, Nelson
    This paper proposes the use of Data Warehousing and Data Mining techniques on performance, social, economic, demographic and cultural data from students who took “Algorithms and Data Structures”, which is a subject in the Information Systems Engineering curricula at UTN-FRRe (Resistencia, Chaco, Argentina), to establish generic academic performance profiles.
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    Academic performance profiles : an intelligent predictive model based on data mining
    (2018-12-01) La Red Martínez, David Luis; Giovannini, Mirtha; Karanik, Marcelo
    It is well known that academic achievement is one of the key aspects in the development of educational activities and it strongly determines the chances of success during and after a university career. It is therefore important to try and effectively monitor students’ performance in order to prevent problems from emerging, as well as, to be able to provide academic coaching when the performance is not adequate. The aforementioned problem-anticipation possibility is closely related to the ability to predict the most probable situation based on concrete information. In an academic achievement framework, it is desirable to be able to predict students’ performance considering concrete individual parameters. This work outlines the results obtained by an academicachievement prediction model based on data mining algorithms which uses socioeconomic information as well as, students’ grades. The tests were carried out at National Technological University, Resistencia Regional Faculty (UTN-FRRe), during the AED-Algoritmos y Estructuras de Datos (Algorithms and Data Structures) class throughout the 2013, 2014, 2015 and 2016 terms. The results obtained confirmed adequate behaviour of the model which has been validated for both description and prediction of academic achievement profiles.
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    Predicción del rendimiento académico con minería de datos buscando reducir el bajo rendimiento académico en asignatura de la UTN – FRRe
    (2017-11-02) La Red Martínez, David Luis; Giovannini, Mirtha; Scappini, Reinaldo
    A menudo, las universidades no son capaces de lidiar con la variedad de factores que pueden afectar el rendimiento académico de los estudiantes. Este tipo de situación genera la necesidad de herramientas que determinen patrones de desempeño académico, y permitan establecer perfiles como base para detectar posibles casos de bajo rendimiento de los estudiantes, es decir, detectar los alumnos que necesitan apoyo en sus actividades académicas.
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    Academic performance problems : a predictive data mining-based model
    (2017-04-01) La Red Martínez, David Luis; Giovannini, Mirtha; Báez, María Eugenia; Torre, Juliana; Yaccuzzi, Nelson
    Often times, universities are not able to deal with the variety of factors that may affect the academic performance of students. This kind of situation generates the need for tools that establish academic performance patterns, setting profiles as a basis to detect potential cases of underachieving students who need support in their academic activities. This paper proposes the use of Data Warehousing and Data Mining techniques on performance, social, economic, demographic and cultural data from students who took “Algorithms and Data Structures”, which is a subject in the Information Systems Engineering curricula at UTN-FRRe (Resistencia, Chaco, Argentina) in an attempt to establish generic academic performance profiles. From the descriptive analysis obtained during the 2013 to 2015 period from the subject aforementioned, a predictive model was used. It establishes the possibility of students' academic failure, taking into account the factors earlier mentioned.
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    Descubrimiento de perfiles de rendimiento estudiantil : un modelo de integración de datos académicos y socioeconómicos
    (2016-10-01) La Red Martínez, David Luis; Karanik, Marcelo; Giovannini, Mirtha; Báez, María Eugenia; Torre, Juliana
    Uno de los mayores problemas que enfrentan las universidades en Argentina, y que cada día toma mayor relevancia, es la alta tasa de deserción estudiantil, la cual se ve reflejada en el número de graduados, que en algunos casos no llega a la mitad de estudiantes. Para encontrar una solución a esta problemática se plantea la necesidad de estudiar sus causas, para lo cual se busca encontrar patrones entre las características de los estudiantes, y definir así perfiles que conduzcan al éxito o fracaso académico. Fundado en esto, este trabajo describe un modelo basado en técnicas de Data Mining para determinar los perfiles de rendimiento académico en la asignatura Algoritmos y Estructura de Datos de la carrera Ingeniería en Sistemas de Información de la Universidad Tecnológica Nacional Facultad Regional Resistencia (UTN-FRRe). Empleando los datos de los alumnos que cursaron la antedicha asignatura en el ciclo lectivo 2014, se procuró determinar en qué medida el desigual desempeño de los mismos es influenciado por otras variables de interés tales como los factores económicos, demográficos, sociales y culturales. En función a estas variables y a partir de técnicas de clasificación y determinación de patrones, se crearon perfiles de rendimiento académico con el objetivo principal de utilizar aquellos tendientes alfracaso o deserción como base a la determinación de futuras políticas de gestión académica que podrían implementarsepara reducir dicho fenómeno.
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    Towards to a predictive model of academic performance using data mining in the UTN-FRRe
    (2016-05-02) La Red Martínez, David Luis; Karanik, Marcelo; Giovaninni, Mirta Eve; Scappini, Reinaldo
    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
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    Perfiles de rendimiento académico : un modelo basado en minería de datos
    (2015-03-01) La Red Martínez, David Luis; Karanik, Marcelo; Giovaninni, Mirta Eve; Pinto, Noelia
    El rendimiento académico es un factor crítico teniendo en cuenta que, frecuentemente, el bajo rendimiento académico está asociado a una alta tasa de deserción. Esto se ha observado en asignaturas del primer nivel de la carrera de Ingeniería en Sistemas de Información (ISI) de la Universidad Tecnológica nacional facultad Regional Resistencia (UTn-fRRe), situada en la ciudad de Resistencia, provincia del Chaco, Argentina, entre ellas Algoritmos y Estructura de datos, donde el bajo rendimiento académico se observa en proporciones muy altas (entre el 60% y el 80% aproximadamente en los últimos años). En este trabajo se propone la utilización de técnicas de minería de datos sobre información del desempeño de los alumnos de la asignatura mencionada con el propósito de caracterizar los perfiles de alumnos exitosos (buen rendimiento académico) y de aquellos que no lo son (bajo rendimiento académico). La determinación de estos perfiles permitiría a futuro definir acciones específicas tendientes a revertir el bajo rendimiento académico, una vez detectadas las variables asociadas al mismo. En este artículo se describen los modelos de datos y de minería de datos utilizados y se comentan los principales resultados obtenidos
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    Academic performance profiles : a descriptive model based on data mining
    (2015-03-10) La Red Martínez, David Luis; Karanik, Marcelo; Giovaninni, Mirta Eve; Pinto, Noelia
    Academic performance is a critical factor considering that poor academic performance is often associated with a high attrition rate. This has been observed in subjects of the first level of Information Systems Engineering career (ISI) of the National Technological University, Resistencia Regional Faculty (UTN-FRRe), situated in Resistencia city, province of Chaco, Argentine. Among them is Algorithms and Data Structures, where the poor academic performance is observed at very high rates (between 60% and about 80% in recent years). In this paper, we propose the use of data mining techniques on performance information for students of the subject mentioned, in order to characterize the profiles of successful students (good academic performance) and those that are not (poor performance). In the future, the determination of these profiles would allow us to define specific actions to reverse poor academic performance, once detected the variables associated with it. This article describes the data models and data mining used and the main results are also commented