Comparative Analysis of Classification Techniques for Predicting Computer Engineering Students’ Academic Performance
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Abstract
The quality of education system affects its every
country’s growth. The top level of quality in the education
system is achieved by extracting useful information for
predictions regarding students’ success rate and factors
affecting student performance. This useful information is
masked in the educational data set and is discovered by
using data mining techniques. An early prediction of student
performance helps authorities to provide extra coaching and
counseling to increase the success rate. In this paper
different classification techniques have been used to
construct student SGPA prediction model based on student’s
social conditions and previous academic performance. Two
algorithms REP Tree and J48 have been exercised on the
236 records of computer engineering students of Punjabi
University to predict the third-semester performance of the
students. J48 gives more accurate results than REP Tree for
student performance prediction. The overall accuracy of J48
is 67.37% and for REP Tree is 56.78%.
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Keywords- data mining; educational data; prediction;
classification.
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