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Improving Student Performance Prediction using Artificial Intelligence

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dc.contributor.author Kerboub, Maria
dc.contributor.author Heriat, Aicha
dc.date.accessioned 2024-07-24T20:06:01Z
dc.date.available 2024-07-24T20:06:01Z
dc.date.issued 2024
dc.identifier.uri https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/8591
dc.description.abstract In recent years, improvements in predicting student performance have garnered significant attention due to their applications in education, personalized academ- ic support, and optimizing educational resources. Predicting student outcomes using artificial intelligence (AI) holds immense potential for teachers to proac- tively identify and support at-risk students. In this thesis, we explore this excit- ing field by employing various machine learning algorithms with an enhanced approach to model optimization. Specifically, we implement hyperparameter tuning using grid search to ensure optimal configurations for four classifiers: Support Vector Machines (SVM), Multilayer Perceptrons (MLP), Decision Trees, and Artificial Neural Networks (ANNs). This comprehensive analysis aims to identify the most effective model for predicting student performance in the selected dataset. The results reveal that the Artificial Neural Network model achieved an accuracy of 98%, demonstrating its superiority in performance pre- diction. EN_en
dc.language.iso en EN_en
dc.publisher University Ghardaia EN_en
dc.subject Student performance prediction, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Decision Tree (DT), Machine Learning, Deep Learning, Grid Search EN_en
dc.title Improving Student Performance Prediction using Artificial Intelligence EN_en
dc.type Thesis EN_en


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