Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/120044
Title: Data-driven System to Predict Academic Grades and Dropout
Author: Rovira, 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: https://doi.org/10.1371/journal.pone.0171207
It is part of: PLoS One, 2017, vol. 12, num. 2, p. e0171207
URI: http://hdl.handle.net/2445/120044
Related resource: https://doi.org/10.1371/journal.pone.0171207
ISSN: 1932-6203
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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