Analysis of Students' Performance by Using Different Data Mining Classifiers

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Hilal Almarabeh 1,*

1. King king Saud Bin Abdulaziz University for Health Sciences College of Science and Health Professions Riyadh, Kingdom of Saudi Arabia

* Corresponding author.


Received: 13 May 2017 / Revised: 28 May 2017 / Accepted: 18 Jun. 2017 / Published: 8 Aug. 2017

Index Terms

Data Mining, Error Measurement, Accuracy, NaiveBayes, Bayesian Net, ID3, J48, Neural Network


Data mining is the analysis of a large dataset to discover patterns and use those patterns to predict the likelihood of the future events. Data mining is becoming a very important field in educational sectors and it holds great potential for the schools and universities. There are many data mining classification techniques with different levels of accuracy. The objective of this paper is to analyze and evaluate the university students' performance by applying different data mining classification techniques by using WEKA tool. The highest accuracy of classifier algorithms depends on the size and nature of the data. Five classifiers are used NaiveBayes, Bayesian Network, ID3, J48 and Neural Network Different performance measures are used to compare the results between these classifiers. The results shows that Bayesian Network classifier has the highest accuracy among the other classifiers.

Cite This Paper

Hilal Almarabeh, "Analysis of Students' Performance by Using Different Data Mining Classifiers", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.8, pp.9-15, 2017. DOI:10.5815/ijmecs.2017.08.02


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