Modelling student dropout using statistical and data mining methods
- https://doi.org/10.2991/amse-19.2019.8How to use a DOI?
- student dropout, logistic regression, decision trees, association rules
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.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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 - Proceedings of the 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 -