Educational data mining: Case study for predicting student dropout in higher education
Date Issued
2015
Author(s)
Nikolovski, Vlatko
Abstract
Reducing the student dropout rate in higher
education is one of the challenges that universities are dealing
with. By providing enriched study programs and qualified teams
of professors, universities aim to enroll more students. Improving
working conditions at the laboratories and other university
resources aims to attract both ambitious students as well as high
quality staff. After enrollment, the main goal of the faculty is to
guide students into successful completion of their studies with the
appropriate knowledge and skills acquired. Nowadays however,
the development and deployment of Student Information Systems
at the universities provides an appropriate infrastructure for
student’s data organization and storage as well as data
acquisition and deeper analyses. This data can help model the
behavior of dropouts, and predict future dropouts, therefore
giving chance to counselors to advise and guide students into
success.
This paper presents various data mining experiments and
results obtained from data for the students from one of the
faculties at the University Ss. Cyril and Methodius in Skopje.
Initially, we give an overview of several data mining algorithms
suitable for analysis of students’ data and dropout prediction.
Furthermore, we explain modifications and applications of the
algorithms over the existing student data. Finally, we provide a
predictive model which will identify a subset of students who
tend to drop out of the studies after the first year. The
classification task aims to identify a pattern among students who
tend to drop out.
education is one of the challenges that universities are dealing
with. By providing enriched study programs and qualified teams
of professors, universities aim to enroll more students. Improving
working conditions at the laboratories and other university
resources aims to attract both ambitious students as well as high
quality staff. After enrollment, the main goal of the faculty is to
guide students into successful completion of their studies with the
appropriate knowledge and skills acquired. Nowadays however,
the development and deployment of Student Information Systems
at the universities provides an appropriate infrastructure for
student’s data organization and storage as well as data
acquisition and deeper analyses. This data can help model the
behavior of dropouts, and predict future dropouts, therefore
giving chance to counselors to advise and guide students into
success.
This paper presents various data mining experiments and
results obtained from data for the students from one of the
faculties at the University Ss. Cyril and Methodius in Skopje.
Initially, we give an overview of several data mining algorithms
suitable for analysis of students’ data and dropout prediction.
Furthermore, we explain modifications and applications of the
algorithms over the existing student data. Finally, we provide a
predictive model which will identify a subset of students who
tend to drop out of the studies after the first year. The
classification task aims to identify a pattern among students who
tend to drop out.
Subjects
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