Modeling the Prediction of Students' Success in the Context of Small Universities
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00503460" target="_blank" >RIV/67985807:_____/18:00503460 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.21125/iceri.2018.1398" target="_blank" >http://dx.doi.org/10.21125/iceri.2018.1398</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21125/iceri.2018.1398" target="_blank" >10.21125/iceri.2018.1398</a>
Alternative languages
Result language
angličtina
Original language name
Modeling the Prediction of Students' Success in the Context of Small Universities
Original language description
The prediction of the success of college students becomes one of the most important but at the same time very demanding themes of university research. Early detection of students at risk of failure is of great importance both for students themselves and for universities that seek to reduce students' failure in courses leading to their early school leaving. Researchers in this field involve methods from the field of classification and regression algorithms or probability models. Frequent interest of researchers is the orientation in the large amount of data currently available to universities through datamining methods. The aim of this paper is to find a method for predict the success of students in the environment of small universities. In this environment, we encounter mainly the problem of the low number of students associated with a small range of the group. This is a case where commonly used methods fail and it is necessary to look for specific approaches that would allow predictions on such limited data. On the other hand, a small number of students give these universities a great advantage in the form of a very effective intervention. Finding suitable methods for modeling student success is therefore very beneficial in this environment.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICERI 2018 Proceedings
ISBN
978-84-09-05948-5
ISSN
2340-1095
e-ISSN
—
Number of pages
10
Pages from-to
1791-1800
Publisher name
IATED Academy
Place of publication
Seville
Event location
Seville
Event date
Nov 12, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000562759301131