Students’ students most at risk. A relatively new

Students’ performance and
achievement are important issues in any university management. Upon completion
of undergraduate program, they can proceed further to pursue into post-graduate
studies, work in the government or private sectors. Thus, student performance
is of dire importance to determine future undertakings upon graduation.
Predicting students’ performances become the key success factor for university
management because good students’ performances mean that the university will
get better ranking amongst universities. Hence, university management must plan
strategic interventions along their study periods to improve the overall achievement
upon graduation.

Data mining is de?ned as the process of
extracting useful and novel information from large amounts of data and is
therefore a valuable tool for converting data into usable information. Data
mining has a wide range of applications in different areas, including
marketing, banking, educational research, surveillance, telecommunications
fraud detection, and scientific discovery (Han & Kamber, 2008) 1. More specifically, data mining can
discover hidden information to inform decision -making in various domains.  The education system is one of these domains
in which the primary concern is the evaluation and, in turn, enhancement of
educational organizations.

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Data mining is a powerful tool for
academic intervention. Through data mining, a university could, for example,
predict with 85 percent accuracy which students will or will not graduate. The
university could use this information to concentrate academic assistance on
those students most at risk.

 

A relatively new ?eld of data mining
applications is Educational Data Mining (EDM). Emerged in 2005, EDM is concerned
with developing data mining techniques to discover knowledge from data obtained
from educational settings 2.

 

Institutions of higher learning such as
universities are at the core of educational systems in which extensive research
and development is performed in a competitive environment. The prerequisite
mission of these institutions is to generate, collect, and share
knowledge.  Specifically, universities
commonly require knowledge mined from past and current data sets that, once
mined, can be used for representing and delivering information to university
administrators for monitoring conditions and taking action to resolve problems.

 

A growing volume of data is currently
stored in educational databases that contain various hidden information that
can help to improve the academic performance of students. Educational data
mining is thus used to study available data and extract the hidden information
for subsequent processes. This hidden information can be used in several
educational processes such as predicting course enrollment, estimating student
dropout rate (Yukselturk, Ozekes, & Turel, 2014) 3, detecting abnormal values in the result sheets of students,
and predicting student performance. Several prediction techniques can be used
to help the educational institutions to predict their students’ Cumulative Grade
Point Averages (CGPAs) at graduation. If this prediction output indicates that
a student will have a low CGPA, then extra efforts can be made to improve the
student’s academic performance and, in turn, his or her CGPA at graduation.

 

The main product of universities are
students. Upon graduation, the students may either continue their studies into
the post-graduate program or become the manpower for the industry, government
and private sectors. Thus, the students’ performances are critical in ensuring
the supply chain is fulfilled.

Data Mining discovers relationships among
attributes in data set, producing conditional statements concerning
attribute-values. Classification is one of the most commonly applied data mining
technique, which uses a set of pre-classified examples to develop a model that can
classify the population of records at large (Samrat and Vikesh, 2012) 4. This study examines four regression
models, that are, multilayer perceptron, linear regression, support vector machine
for regression(SMOreg), K-nearest neighbors(KNN) using WEKA in predicting the
final cumulative grade point average (CGPA) of the students upon graduation. Results
arising from this study provide important reference materials for the planning
of the future success of the students’ and the university. Dire attrition

Students’ performance and
achievement are important issues in any university management. Upon completion
of undergraduate program, they can proceed further to pursue into post-graduate
studies, work in the government or private sectors. Thus, student performance
is of dire importance to determine future undertakings upon graduation.
Predicting students’ performances become the key success factor for university
management because good students’ performances mean that the university will
get better ranking amongst universities. Hence, university management must plan
strategic interventions along their study periods to improve the overall achievement
upon graduation.

Data mining is de?ned as the process of
extracting useful and novel information from large amounts of data and is
therefore a valuable tool for converting data into usable information. Data
mining has a wide range of applications in different areas, including
marketing, banking, educational research, surveillance, telecommunications
fraud detection, and scientific discovery (Han & Kamber, 2008) 1. More specifically, data mining can
discover hidden information to inform decision -making in various domains.  The education system is one of these domains
in which the primary concern is the evaluation and, in turn, enhancement of
educational organizations.

 

Data mining is a powerful tool for
academic intervention. Through data mining, a university could, for example,
predict with 85 percent accuracy which students will or will not graduate. The
university could use this information to concentrate academic assistance on
those students most at risk.

 

A relatively new ?eld of data mining
applications is Educational Data Mining (EDM). Emerged in 2005, EDM is concerned
with developing data mining techniques to discover knowledge from data obtained
from educational settings 2.

 

Institutions of higher learning such as
universities are at the core of educational systems in which extensive research
and development is performed in a competitive environment. The prerequisite
mission of these institutions is to generate, collect, and share
knowledge.  Specifically, universities
commonly require knowledge mined from past and current data sets that, once
mined, can be used for representing and delivering information to university
administrators for monitoring conditions and taking action to resolve problems.

 

A growing volume of data is currently
stored in educational databases that contain various hidden information that
can help to improve the academic performance of students. Educational data
mining is thus used to study available data and extract the hidden information
for subsequent processes. This hidden information can be used in several
educational processes such as predicting course enrollment, estimating student
dropout rate (Yukselturk, Ozekes, & Turel, 2014) 3, detecting abnormal values in the result sheets of students,
and predicting student performance. Several prediction techniques can be used
to help the educational institutions to predict their students’ Cumulative Grade
Point Averages (CGPAs) at graduation. If this prediction output indicates that
a student will have a low CGPA, then extra efforts can be made to improve the
student’s academic performance and, in turn, his or her CGPA at graduation.

 

The main product of universities are
students. Upon graduation, the students may either continue their studies into
the post-graduate program or become the manpower for the industry, government
and private sectors. Thus, the students’ performances are critical in ensuring
the supply chain is fulfilled.

Data Mining discovers relationships among
attributes in data set, producing conditional statements concerning
attribute-values. Classification is one of the most commonly applied data mining
technique, which uses a set of pre-classified examples to develop a model that can
classify the population of records at large (Samrat and Vikesh, 2012) 4. This study examines four regression
models, that are, multilayer perceptron, linear regression, support vector machine
for regression(SMOreg), K-nearest neighbors(KNN) using WEKA in predicting the
final cumulative grade point average (CGPA) of the students upon graduation. Results
arising from this study provide important reference materials for the planning
of the future success of the students’ and the university. Dire attrition

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