Data-driven system to predict academic grades and dropout

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 pers...

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Bibliographic Details
Published in:PloS one 2017-02, Vol.12 (2), p.e0171207-e0171207
Main Authors: Rovira, Sergi, Puertas, Eloi, Igual, Laura
Format: Article
Language:English
Subjects:
Academic achievement
Adolescent
Adult
Aprenentatge automàtic
Artificial intelligence
Biology and Life Sciences
Collaboration
Computer and Information Sciences
Computer science
Dropouts
Engineering and Technology
Female
Grades (Scholastic marks)
Higher education
Humans
Information processing
Information systems
Learning
Learning algorithms
Machine Learning
Male
Models, Theoretical
Motivation
Physical Sciences
Recommender systems
Regression analysis
Rendiment acadèmic
Research and Analysis Methods
School dropout programs
School dropouts
Social Sciences
Student Dropouts
Student retention
Students
Studies
Trends
Tutoring
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0171207