Education is essential and increasingly crucial for the development of almost all countries worldwide. As educational data has become increasingly available, scholars have shown a growing interest in exploring the correlation between students' academic achievements and other factors that may impact their performance using machine learning algorithms. This research paper introduces a novel hybrid classifier that aims to predict the academic performance of students by using a combination of different single algorithms. The proposed hybrid classifier (PHC) is compared to six available classification algorithms (random forest (RF), C4.5, classification and regression trees (CART), support vector machines (SVM), naive Bayes (NB) and K-nearest neighbors (KNN)) using recall, precision, F1-score, and accuracy evaluation measures. Our experimental results reveal that the PHC classifier consistently outperforms the individual classifiers across multiple evaluation metrics. Specifically, the PHC classifier achieved an accuracy rate of 92.40%, surpassing the RF, C4.5, and CART classifiers, which were the next best performers. In terms of precision and F1 score, the PHC also demonstrated superior performance, indicating its robustness in correctly identifying positive instances and providing balanced accuracy. While the C4.5 classifier performed comparably to the PHC classifier concerning the recall metric, the hybrid model's overall performance highlights its effectiveness in leveraging the complementary strengths of the included classifiers. The suggested hybrid model has the potential to enhance students' academic performance and success more effectively and efficiently. It could benefit students, educators, and academic institutions. Additionally, it provides practical insights for educators and institutions striving to improve student achievement using predictive analysis.
Citation: Abdulmajeed Atiah Alharbi, Jeza Allohibi. A new hybrid classification algorithm for predicting student performance[J]. AIMS Mathematics, 2024, 9(7): 18308-18323. doi: 10.3934/math.2024893
Education is essential and increasingly crucial for the development of almost all countries worldwide. As educational data has become increasingly available, scholars have shown a growing interest in exploring the correlation between students' academic achievements and other factors that may impact their performance using machine learning algorithms. This research paper introduces a novel hybrid classifier that aims to predict the academic performance of students by using a combination of different single algorithms. The proposed hybrid classifier (PHC) is compared to six available classification algorithms (random forest (RF), C4.5, classification and regression trees (CART), support vector machines (SVM), naive Bayes (NB) and K-nearest neighbors (KNN)) using recall, precision, F1-score, and accuracy evaluation measures. Our experimental results reveal that the PHC classifier consistently outperforms the individual classifiers across multiple evaluation metrics. Specifically, the PHC classifier achieved an accuracy rate of 92.40%, surpassing the RF, C4.5, and CART classifiers, which were the next best performers. In terms of precision and F1 score, the PHC also demonstrated superior performance, indicating its robustness in correctly identifying positive instances and providing balanced accuracy. While the C4.5 classifier performed comparably to the PHC classifier concerning the recall metric, the hybrid model's overall performance highlights its effectiveness in leveraging the complementary strengths of the included classifiers. The suggested hybrid model has the potential to enhance students' academic performance and success more effectively and efficiently. It could benefit students, educators, and academic institutions. Additionally, it provides practical insights for educators and institutions striving to improve student achievement using predictive analysis.
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Abdulmajeed Atiah Alharbi, Jeza Allohibi. A new hybrid classification algorithm for predicting student performance[J]. AIMS Mathematics, 2024, 9(7): 18308-18323. doi: 10.3934/math.2024893
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