OPTIMAL ALGORITHM FOR PREDICTING STUDENTS’ ACADEMIC PERFORMANCE
Authors:
OKOLIE Samuel
Publication Type: Journal article
Journal:
ISSN Number:
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Abstract
Machine learning has been successfully applied to
numerous domains such as pattern recognition, image
recognition, fraud detection, medical diagnosis,
banking, bioinformatics, commodity trading, computer
games and various control applications. Recently, this
paradigm is been employed to enhance and evaluate
higher education tasks. The focus of this work is on
identifying the optimal algorithm suitable for predicting
first-year tertiary students academic performance based
on their family background factors and previous
academic achievement. One thousand five hundred
(1,500) enrolment records of students admitted into
computer science programme Babcock University,
Nigeria between 2001 and 2010 was used. The students’
first year academic performance was measured by
Cumulative Grade Point Average (CGPA) at the end of
the first session and the previous academic achievement
was measured by SSCE grade score and UME score.
Waikato Environment for Knowledge Analysis
(WEKA) was used to generate 10 classification models(
five decision tree algorithms -Random forest, Random
tree, J48, Decision stump and REPTree and five rule
induction algorithms –JRip, OneR, ZeroR, PART, and
Decision table) and a multilayer perceptron, an
artificial neural network function. These algorithms
were compared using 10-fold cross validation and holdout
method considering accuracy level, confusion
matrices and CPU time to determine the optimal model.
This work will be taken further by designing a
framework of predictive system based on the rules
generated from the optimal model.