Title: Multimodal fake data detection and filtering using GANs and contrast learning

Authors: Yuanjie Zou

Addresses: School of Software, Hunan Vocational College of Science and Technology, Changsha 410004, Hunan, China

Abstract: In recent years, artificial intelligence (AI) has become an integral part of online education, improving ITS, online courses, and learning management systems (LMS). Online education is predicting students' knowledge acquisition based on clickstream data. The lack of focus on student interaction with the content and quizzes offered in lecture videos is a major hurdle to online education. Therefore, this paper proposes a multimodal fake data detection and filtering-based generative adversarial network (MFDDF-GAN) to predict student performance in online learning. MFDDF-GAN aims to ensure that all material used in online education is authentic, of high quality, has protected users, is effective in communication. This MFDDF-GAN approach takes advantage of the information already included in the click sequences rather than relying on characteristics. The experimental results show that the MFDDF-GAN technique produces actionable insights into learning analytics related to video-watching learning performance and knowledge acquisition.

Keywords: online learning; generative adversarial networks; GANs; support vector machine; SVM; student learning performance.

DOI: 10.1504/IJIIDS.2025.147420

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.3/4, pp.361 - 386

Received: 13 Mar 2024
Accepted: 20 Jun 2024
Published online: 15 Jul 2025 *

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