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On Data Protection Regulations, Big Data and Sledgehammers in Higher Education

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Research Group of Artificial Intelligence and Assistance Technology (GIIATA), Universidad Politécnica Salesiana, Cuenca 010102, Ecuador
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AtlantTIC Research Center, Department of Telematics Engineering, University of Vigo, 36310 Vigo, Spain
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Deicom Technologies S.L., 36203 Vigo, Spain
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ΓAB LAB—Knowledge and Uncertainty Research Laboratory, Department of Informatics and Telecommunications, University of Peloponnese, 22100 Tripoli, Greece
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Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(15), 3084; https://doi.org/10.3390/app9153084
Received: 25 June 2019 / Revised: 17 July 2019 / Accepted: 27 July 2019 / Published: 31 July 2019
(This article belongs to the Section Computing and Artificial Intelligence)
Universities in Latin America commonly gather much more information about their students than allowed by data protection regulations in other parts of the world. We have tackled the question of whether abundant socio-economic data can be harnessed for the purpose of predicting academic outcomes and, thereby, taking proactive actions in student attention, course planning and resource management. A study was conducted to analyze the data gathered by a private university in Ecuador over more than 20 years, to normalize them and to parameterize a Multi-Layer Perceptron neural network, whose best-performing configuration would be used as a benchmark for the comparison of more recent and sophisticated Artificial Intelligence techniques. However, an extensive scan of hyperparameters for the perceptron—exploring more than 12,000 configurations—revealed no significant relationships between the input variables and the chosen metrics, suggesting that there is no gain from processing the extensive socio-economic data. This finding contradicts the expectations raised by previous works in the related literature and in some cases highlights important methodological flaws. View Full-Text
Keywords: data protection; student records; performance prediction; Multi-Layer Perceptron; deep learning data protection; student records; performance prediction; Multi-Layer Perceptron; deep learning
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MDPI and ACS Style

García-Vélez, R.A.; López-Nores, M.; González-Fernández, G.; Robles-Bykbaev, V.E.; Wallace, M.; Pazos-Arias, J.J.; Gil-Solla, A. On Data Protection Regulations, Big Data and Sledgehammers in Higher Education. Appl. Sci. 2019, 9, 3084. https://doi.org/10.3390/app9153084

AMA Style

García-Vélez RA, López-Nores M, González-Fernández G, Robles-Bykbaev VE, Wallace M, Pazos-Arias JJ, Gil-Solla A. On Data Protection Regulations, Big Data and Sledgehammers in Higher Education. Applied Sciences. 2019; 9(15):3084. https://doi.org/10.3390/app9153084

Chicago/Turabian Style

García-Vélez, Roberto A., Martín López-Nores, Gabriel González-Fernández, Vladimir E. Robles-Bykbaev, Manolis Wallace, José J. Pazos-Arias, and Alberto Gil-Solla. 2019. "On Data Protection Regulations, Big Data and Sledgehammers in Higher Education" Applied Sciences 9, no. 15: 3084. https://doi.org/10.3390/app9153084

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