Facultad Regional Resistencia

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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.