Improving University Operations with Data Mining: Predicting Student Performance (CROSBI ID 609990)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Dragičević, Mladen ; Pejić Bach, Mirjana ; Šimičević, Vanja
engleski
Improving University Operations with Data Mining: Predicting Student Performance
The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.
Data mining ; knowledge discovery in databases ; prediction models ; student success
International Conference on Management Technology and Applications (ICMTA 2014) : proceedings
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Podaci o prilogu
556-571.
2014.
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objavljeno
Podaci o matičnoj publikaciji
World academy of science, engineering and technology
Firenza : München: World Academy of Science, Engineering and Technology (WASET)
1307-6892
Podaci o skupu
ICMTA 2014: International Conference on Management Technology and Applications
predavanje
14.04.2014-15.04.2014
Venecija, Italija