BERKA, Petr, Lubos MAREK and Michal VRABEC. Modeling Students Dropout Using Statistical and Data Mining Methods. In Boda, M., Mendelova, V., Grausova, M. Proceedings of 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics. Paris: Atlantis Press. p. 70-80. ISBN 978-94-6252-804-8. doi:10.2991/amse-19.2019.8. 2019.
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Basic information
Original name Modeling Students Dropout Using Statistical and Data Mining Methods
Name in Czech Modelování studijní neúspěšnosti pomocí statisitckých a data miningových metod
Authors BERKA, Petr (203 Czech Republic, guarantor, belonging to the institution), Lubos MAREK (203 Czech Republic) and Michal VRABEC (203 Czech Republic).
Edition Paris, Proceedings of 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics, p. 70-80, 11 pp. 2019.
Publisher Atlantis Press
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10200 1.2 Computer and information sciences
Country of publisher France
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW Celý text článku
RIV identification code RIV/04274644:_____/19:#0000589
Organization unit University of Finance and Administration
ISBN 978-94-6252-804-8
ISSN 2589-6644
Doi http://dx.doi.org/10.2991/amse-19.2019.8
UT WoS 000558637800008
Keywords (in Czech) studijní neúspěšnost, logistická regrese, rozhodovací stromy, asociační pravidla
Keywords in English student dropout; logistic regression; decision trees; association rules
Tags AR 2019-2020, POZNÁMKA, RIV_2021, unor_2020_o, xD1_překvalifikování, xD2
Tags International impact, Reviewed
Changed by Changed by: Bc. Jan Peterec, učo 24999. Changed: 14/4/2021 08:22.
Abstract
Not completing the study by a large portion of students is a serious problem at the universities worldwide. Regardless of the countries, the numbers are very similar: about one-half of students who enrolled for the bachelor study leave the 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 the university. Models created using traditional statistical analysis techniques (logistic regression) are compared with models created using data mining methods (decision trees, rules).
Abstract (in Czech)
Studijní neúspěšnost velkého počtu studentů je závažný problém na vysokých školách na celém světě. Cílem naší studie bylo vytvořit model, který by rozlišoval mezi studenty, kteří úspěšně dokončí svá studia a studenty, kteří školu opustí předčasně. Porovnávali jsem klasické, regresní modely a modely založené na rozhodovacích stromech a asociačních pravidlech.
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