Advancing student outcome predictions through generative adversarial networks

Predicting student outcomes is essential in educational analytics for creating personalised learning experiences. The effectiveness of these predictive models relies on having access to sufficient and accurate data. However, privacy concerns and the lack of student consent often restrict data collec...

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Bibliographic Details
Main Authors: Helia Farhood, Ibrahim Joudah, Amin Beheshti, Samuel Muller
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Computers and Education: Artificial Intelligence
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2666920X24000961
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