Facultad Regional Resistencia
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Item 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, NelsonOften 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.Item Educational data mining : an experience in UTN FRRe(2018-03-13) La Red Martínez, David Luis; Giovannini, Mirtha; Báez, María Eugenia; Torre, Juliana; Yaccuzzi, NelsonThis 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.Item 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, ReinaldoStudents 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