Prediction of student’s performance through educational data mining techniques

Nibras Z. Salih, Walaa Khalaf


Many educators have worried about the failures of students through academic education. Thus, a variety of predictions have been applied to general information including culture, social, and economic information which wasn’t related to student performance. We have gathered an actual dataset from three years of academic stages of Mustansiriyah University in Iraq. The dataset consists of academic information without any socioeconomic data, it includes forty-four undergraduate students with thirteen attributes. We have proposed a model that explains the correlation between two main subjects which are, mathematics, and control systems. This study aimed to identify student failure of the control systems subject in the third year depending on the academic features of the mathematics subjects in the first and second years. Three algorithms were applied to the dataset including Naïve Bayes, support vector machine, and multilayer perceptron. Since the dataset was imbalanced, this leads to appear overfitting problem in the results so the synthetic minority oversampling technique was utilized to solve this problem. Our results show that the support vector machine algorithm proves an efficient classification after applied synthetic minority oversampling technique. The accuracy of the classifiers was measured from the confusion matrix using the Waikato environment for knowledge analysis (WEKA) tool and its related metrics.


classification algorithms; cross-validation; imbalance datasets; synthetic minority; oversampling technique;

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