Evaluation of students' performance during the academic period using the XG-Boost Classifier-Enhanced AEO hybrid model

The proactive prediction and systematic classification of students' academic performance empower educational administrators with the invaluable capability to not only pinpoint potential challenges but also to craft targeted strategies and interventions that are tailored to the unique needs of s...

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Vydáno v:	Expert systems with applications Ročník 238; s. 122136
Hlavní autoři:	Cheng, Biqian, Liu, Yuping, Jia, Yunjian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.03.2024
Témata:	Machine learning methods Metaheuristic optimization algorithms Student performance evaluation XG-Boost Classifier Metaheuristic optimization algorithms XG-Boost Classifier Student performance evaluation Machine learning methods
ISSN:	0957-4174, 1873-6793
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Popis
Shrnutí:	The proactive prediction and systematic classification of students' academic performance empower educational administrators with the invaluable capability to not only pinpoint potential challenges but also to craft targeted strategies and interventions that are tailored to the unique needs of students. This multifaceted approach enables educational institutions to proactively address issues within the education system, fostering a more equitable and effective learning environment for all, while simultaneously fostering a culture of continuous improvement and accountability in the pursuit of educational excellence. Hence, the current investigation aims to classify and predict the students' performance by examining and comparing the machine learning and artificial neural network assessments. Five methods of the Random Forest Classifier, the Decision Tree Classifier, the K Neighbors Classifier, the MLP Classifier, and the XG-Boost Classifier are used. These methods' performances are compared through the accuracy, precision, recall, and F1-score indicators. This comparison is applied to the base data and balanced data, which is carried out by the SVM-SMOTE technique. Finally, five metaheuristic algorithms are applied to the selected method to evaluate the performance indicators of the hybrid models. The results indicate that applying the SVM-SMOTE technique improves the methods' performance, in which the XG-Boost represented the best performance. As a result, the metaheuristic algorithms are applied to the XG-Boost, yielding to 9.33%, 8.44%, 9.33%, and 9.27% enhancement of the Accuracy, Precision, Recall and F1-Score values. Subsequently, the Enhanced Artificial Ecosystem-Based Optimization +XG-Boost hybrid method provides the accuracy and F1-score values of 0.9417 and 0.9413. These results underscore the potential of combining machine learning techniques with metaheuristic algorithms to enhance the accuracy and effectiveness of predicting and classifying students' performance, thus providing valuable insights for educational administrators to address issues and improve the education system.
ISSN:	0957-4174 1873-6793
DOI:	10.1016/j.eswa.2023.122136