Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques

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Ahmed Mueen 1,* Bassam Zafar 1 Umar Manzoor 1

1. King Abdulaziz University, Saudi Arabia, Jeddah

* Corresponding author.


Received: 6 Aug. 2016 / Revised: 16 Sep. 2016 / Accepted: 2 Oct. 2016 / Published: 8 Nov. 2016

Index Terms

Educational Data Mining, Classification, Academic performance prediction, Knowledge Discovery


The main objective of this study is to apply data mining techniques to predict and analyze students' academic performance based on their academic record and forum participation. Educational Data Mining (EDM) is an emerging tool for academic intervention. The educational institutions can use EDU for extensive analysis of students’ characteristics. In this study, we have collected students’ data from two undergraduate courses. Three different data mining classification algorithms (Naïve Bayes, Neural Network, and Decision Tree) were used on the dataset. The prediction performance of three classifiers are measured and compared. It was observed that Naïve Bayes classifier outperforms other two classifiers by achieving overall prediction accuracy of 86%. This study will help teachers to improve student academic performance.

Cite This Paper

Ahmed Mueen, Bassam Zafar, Umar Manzoor, "Modeling and Predicting Students' Academic Performance Using Data Mining Techniques", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.11, pp.36-42, 2016. DOI:10.5815/ijmecs.2016.11.05


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