Early Warning System for Academic using Data Mining

Santoso, Leo Willyanto (2019) Early Warning System for Academic using Data Mining. [UNSPECIFIED]

[thumbnail of ICACCA_Full_Paper.pdf]
Preview
PDF
ICACCA_Full_Paper.pdf

Download (763kB)
[thumbnail of Plagiarism Check]
Preview
PDF (Plagiarism Check)
ICACCA_Plagiarism.pdf

Download (979kB)
[thumbnail of peer review]
Preview
PDF (peer review)
17_peerreview_early_warning_system.pdf

Download (1MB)
[thumbnail of Cek plagiasi - Leo]
Preview
PDF (Cek plagiasi - Leo)
15._Early_Warning_System_-_CP.pdf

Download (846kB)
Official URL: http://www.icacca.in/

Abstract

Nowadays, student academic data in universities are very huge. However, the opportunity to manage the data is a knowledge that cannot be overlooked. Educational data mining is a current research field which uses data mining algorithms to transform large volumes of academic data into valuable knowledge capable of improving the educational processes and decisions. This research makes use of a set of three models. The first two models used the data obtained in the first year (first semester and second semester), to predict the academic success of the enrolled students, while the third model used the information available at the end of the first year to predict the academic performances of the students at the end of their study. At the same time, this work also intends to identify the factors that are most critical to these models. The results of this research paved way for the head of the school to identify students in need of more pedagogical support, as well as students with high probability of excelling in their studies. It could also allow them to focus their attention on the critical aspects, by implementing mechanisms that tackles students� difficulties

Item Type: UNSPECIFIED
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Industrial Technology > Informatics Engineering Department
Depositing User: Admin
Date Deposited: 27 Aug 2019 14:04
Last Modified: 11 Jan 2023 09:14
URI: https://repository.petra.ac.id/id/eprint/18440

Actions (login required)

View Item
View Item