Predicting student performance using decision trees (CROSBI ID 590311)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Dragičević, Mladen ; Pejić Bach, Mirjana ; Šimičević, Vanja
engleski
Predicting student performance using decision trees
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 on 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 usage in decision support systems.
knowledge discovery in databases ; data mining ; student success ; prediction models ; evaluation of models ; classification
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Podaci o prilogu
186-191.
2012.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the IBC 2012 Internet & Business Conference
Ivkovic, Miodrag ; Šimičević. Vanja ; Pejić Bach, Mirjana
Zagreb: BIT udruga
Podaci o skupu
IBC 2012 Internet & Business Conference
predavanje
27.06.2012-28.06.2012
Rovinj, Hrvatska