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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