Modeling Students Dropout Using Statistical and Data Mining Methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F19%3A%230000589" target="_blank" >RIV/04274644:_____/19:#0000589 - isvavai.cz</a>
Result on the web
<a href="https://www.atlantis-press.com/proceedings/amse-19/125919265" target="_blank" >https://www.atlantis-press.com/proceedings/amse-19/125919265</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2991/amse-19.2019.8" target="_blank" >10.2991/amse-19.2019.8</a>

Result language
angličtina
Original language name
Modeling Students Dropout Using Statistical and Data Mining Methods
Original language description
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).
Czech name
—
Czech description
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics
ISBN
9789462528048
ISSN
2589-6644
e-ISSN
—
Number of pages
11
Pages from-to
70-80
Publisher name
Atlantis Press
Place of publication
Paris
Event location
Nižná
Event date
Aug 28, 2019
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000558637800008

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