Students' Performance Prediction Using Machine Learning Based on Generative Adversarial Network
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Predicting student performance is a crucial area of research in the field of education. To improve the accuracy and reliability of student performance prediction, machine learning (ML) techniques have been widely used. In this study, we propose a novel approach for predicting student performance using five ML techniques, which include data analysis, pre-processing techniques, and data augmentation using GAN. We evaluate the proposed approach using a real-world dataset of student academic records and compare the results to those obtained without data augmentation. Our findings demonstrate that data augmentation significantly improves the accuracy and reliability of student performance prediction. Specifically, the random forest classifier achieves the best accuracy of 99.8%. This research contributes to the field of education by providing a more comprehensive and accurate model for predicting student performance, which can support informed decision-making and improve educational outcomes. © 2023 IEEE.
Açıklama
5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 -- 8 June 2023 through 10 June 2023 -- Istanbul -- 190025
Anahtar Kelimeler
classification, GAN, Machine Learning, Student's performance
Kaynak
HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
