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Título
Predicting academic success through students’ interaction with Version Control Systems
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Open Computer Science
Número de la revista
1
Datos de la obra
Guerrero-Higueras, Á. M., Decastro-García, N., Rodriguez-Lera, F. J., Matellán, V., and Conde, M. Á. (2019). Predicting academic success through students” interaction with Version Control Systems. Open Computer Science, 9(1), 243-251. https://doi.org/10.1515/COMP-2019-0012
Editor
De Gruyter
Fecha
2019-07-23
Résumé
[EN] Version Control Systems are commonly used by
Information and communication technology professionals.
These systems allow monitoring programmers activity
working in a project. Thus, Version Control Systems are
also used by educational institutions. The aim of thiswork
is to evaluate if the academic success of students may be
predicted by monitoring their interaction with a Version
Control System. In order to do so, we have built a Machine
Learning modelwhich predicts student results in a specific
practical assignment of the Operating Systems Extension
subject, from the second course of the degree in Computer
Science of the University of León, through their interaction
with a Git repository. To build the model, several classifiers
and predictors have been evaluated. In order to do
so, we have developed Model Evaluator (MoEv), a tool to
evaluate Machine Learning models in order to get the most
suitable for a specific problem. Prior to the model development,
a feature selection from input data is done. The
resulting model has been trained using results from 2016–
2017 course and later validated using results from 2017–
2018 course. Results conclude that the model predicts students’
success with a success high percentage.
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