Proceedings of the 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics (AMSE 2019)

Modelling student dropout using statistical and data mining methods

Authors
Petr Berka, Luboš Marek, Michal Vrabec
Corresponding Author
Petr Berka
Available Online October 2019.
DOI
https://doi.org/10.2991/amse-19.2019.8How to use a DOI?
Keywords
student dropout, logistic regression, decision trees, association rules
Abstract
Not completing the study by a large portion of students is a serious problem at universities worldwide. Regardless of the country, numbers are very similar: about one-half of students who enrolled for the bachelor study leave university before obtaining the degree. To deal with this problem, we create models to distinguish between students who successfully completed their study and students who dropped out of university. Models created using traditional statistical analysis techniques (logistic regression) are compared with models created using data mining methods (decision trees, association rules). We use data about students who enrolled for their bachelor study at the University of Economics in Prague in the academic year 2013/2014 in our analysis.
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Proceedings
22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics (AMSE 2019)
Part of series
Atlantis Studies in Uncertainty Modelling
Publication Date
October 2019
ISBN
978-94-6252-804-8
ISSN
2589-6644
DOI
https://doi.org/10.2991/amse-19.2019.8How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Petr Berka
AU  - Luboš Marek
AU  - Michal Vrabec
PY  - 2019/10
DA  - 2019/10
TI  - Modelling student dropout using statistical and data mining methods
BT  - 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics (AMSE 2019)
PB  - Atlantis Press
SP  - 70
EP  - 80
SN  - 2589-6644
UR  - https://doi.org/10.2991/amse-19.2019.8
DO  - https://doi.org/10.2991/amse-19.2019.8
ID  - Berka2019/10
ER  -