Student Academic Performance Prediction using Supervised Learning Techniques

Muhammad Imran, Shahzad Latif, Danish Mehmood, Muhammad Saqlain Shah


Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model.

Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems.

This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.


Educational Data mining, Educational Data Mining, Predicting Student Performance, Decision Tree, Ensemble

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Copyright (c) 2019 Muhammad Imran, Muhammad Saqlain Shah, shahzad latif, Danish Mehmood

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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