Tên bài báo:

Training neural networks to predict student academic performance: A comparison of cuckoo search and gravitational search algorithms
Tác giả:
Đỗ Quang Hưng
Tham gia cùng:
Tạp chí:
International Journal of Computational Intelligence and Applications
Năm xuất bản:
Từ trang 253 đến trang 259
Lĩnh vực:
Công nghệ thông tin
Phạm vi:
Quốc tế

Tóm tắt:

The accurate prediction of students' academic performance is of importance to institutions as it provides valuable information for decision making in the admission process and enhances educational services by allocating customized assistance according to the predicted performance. The purpose of this study is to investigate the prediction ability of neural networks trained by two recent heuristic algorithms inspired by the behaviors of natural phenomena, namely, the cuckoo search and gravitational search algorithms. We used previous exam results and other factors, such as the location of the student's high school and the student's gender, as input variables, and predicted the student's expected performance. The cuckoo search and gravitational search algorithms were utilized to train the feed-forward neural network for prediction. These algorithms optimized the weights between layers and biases of the neuron network. The simulation results of the two algorithms were then discussed and analyzed. It was found that the neural network trained by the cuckoo search could be used in the prediction of students' academic performance. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

Từ khóa:

Cuckoo search; gravitational search algorithm; heuristic algorithms; neural networks; educational institutions
Thông tin tác giả
Đỗ Quang Hưng

Đỗ Quang Hưng

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