Enhanced Machine Learning model for Student Academic Performance Prediction using Genetic algorithm and compare with CNN model


  • Department of Cmputer Science , College of Eduction for Pure Science, University of Thi-Qar


Prediction, Machine Learning, CNN, SVM, LR, ANN, Genetic algorithm


In this paper, a comparison between various machine learning algorithms for prediction of student
academic performance has been achieved. Students’ data is being acquired many resources such as elearning
resources, online and virtual courses and institutional technology. Many researches might use the
collected data to find out and comprehend students’ behaviors toward learning. The obtained data need to
be preprocessed then used to build prediction model. The achieved models are Support Vector Machine
(SVM), Logistic Regression (LR), Artificial Neural Network (ANN) with accuracies 99.99 %, 100 % and
99.99 % respectively. Genetic algorithm was used to adapt parameters of the algorithms to give best
accuracy. OULAD dataset was used for validation. Results showed that these algorithms are sufficient for
prediction of student academic performance and CNN is not needed for this purpose.


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