Facultad Regional Resistencia

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Author: search.filters.author.Karanik, MarceloSubject: search.filters.subject.data mining

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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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    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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    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
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    Academic performance profiles in algorithms and data structures of UTN
    (2015-07-12) 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 Algorithms and Data Structures of Information Systems Engineering career (ISI) of the National Technological University, Resistencia Regional Faculty (UTNFRRe), situated in Resistencia city, province of Chaco, Argentine, 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). This article describes the data models and data mining used and the main results are also commented.