Applying advanced linear models in the task of predicting student success (CROSBI ID 663112)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Glavaš, Marko ; Brkić Bakarić, Marija ; Matetić, Maja
engleski
Applying advanced linear models in the task of predicting student success
The paper presents a comparative analysis of different linear models based on the Moodle data related to the course Programming 2 at the Department of Informatics, University of Rijeka. The task is to predict student final course success based on student activity represented by the initial set of features. We experiment with several methods with the aim to reduce the feature set in order to extract the most representative features thus increasing prediction accuracy. The interpretation of the obtained predictive models is given.
Linear model, regularization, prediction of student success, lasso regression, ridge regression
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Podaci o prilogu
820-824.
2018.
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objavljeno
Podaci o matičnoj publikaciji
Proceedings of 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
Biljanović, Petar
Opatija: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO
Podaci o skupu
MIPRO 2018
predavanje
21.05.2018-25.05.2018
Opatija, Hrvatska
Povezanost rada
Računarstvo, Informacijske i komunikacijske znanosti