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Title: Data-driven System to Predict Academic Grades and Dropout
Author: Rovira Cisterna, Sergi
Puertas i Prats, Eloi
Igual Muñoz, Laura
Keywords: Aprenentatge automàtic
Rendiment acadèmic
Machine learning
Academic achievement
Issue Date: 14-Feb-2017
Publisher: Public Library of Science (PLoS)
Abstract: Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona.
Note: Reproducció del document publicat a:
It is part of: PLoS One, 2017, vol. 12, num. 2, p. e0171207
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ISSN: 1932-6203
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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