Clustering of imbalanced moodle data for early alert of student failure (CROSBI ID 640215)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Šišovic, Sabina ; Matetić, Maja ; Brkić Bakarić, Marija
engleski
Clustering of imbalanced moodle data for early alert of student failure
This paper is an attempt of applying EDM methods on Moodle data in order to detect specific behaviours within student groups with the tendency to fail the course. The research is conducted on Moodle logs gathered in the blended course Programming 1. Extracting and using crucial information on time can be a turning point for students in at-risk stage, which is what we tried to achieve in this research.
educational data mining ; clustering ; e-learning ; early alert ; Moodle ; K-means
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Podaci o prilogu
165-170.
2016.
objavljeno
10.1109/SAMI.2016.7423001
Podaci o matičnoj publikaciji
IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI)
Herl’any, Slovakia:
978-1-4673-8739-2
Podaci o skupu
International Symposium on Applied Machine Intelligence and Informatics (SAMI)
predavanje
21.01.2016-23.01.2016
Herľany, Slovačka
Povezanost rada
Informacijske i komunikacijske znanosti, Računarstvo