Article: Modelling and trend analysis of student idea propagation paths facilitated by intelligent recommendation algorithms Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.30 pp.113 - 127 Abstract: This study develops a propagation path modelling method integrating implicit semantic analysis and ST-GCNs to examine how intelligent recommendation algorithms govern student idea dissemination. By constructing an algorithm-user-content dynamic interaction model, we quantify the impact of recommendation strategies on idea diffusion pathways within campus networks. Our analysis reveals a distinctive 'centralised-jumping' dual-mode evolution characterising the diffusion process. Validation using a publicly available social media dataset demonstrates a 12.7 percentage-point improvement in model accuracy over traditional methods. Furthermore, we find that educational level significantly alters path selection probability. This research provides educators and administrators with a theoretical framework and quantitative analytical tool for designing targeted information intervention strategies. Inderscience Publishers - linking academia, business and industry through research

Title: Modelling and trend analysis of student idea propagation paths facilitated by intelligent recommendation algorithms

Authors: Zhuoya Guo; Hui Shen; Yingxi Zhang

Addresses: College of Culture and Media, Kaifeng Vocational College of Culture and Arts, Kaifeng 475000, China ' Modern Education Technology Center, Kaifeng Vocational College of Culture and Arts, Kaifeng 475000, China ' Cheongju University, Cheongju 363170, South Korea

Abstract: This study develops a propagation path modelling method integrating implicit semantic analysis and ST-GCNs to examine how intelligent recommendation algorithms govern student idea dissemination. By constructing an algorithm-user-content dynamic interaction model, we quantify the impact of recommendation strategies on idea diffusion pathways within campus networks. Our analysis reveals a distinctive 'centralised-jumping' dual-mode evolution characterising the diffusion process. Validation using a publicly available social media dataset demonstrates a 12.7 percentage-point improvement in model accuracy over traditional methods. Furthermore, we find that educational level significantly alters path selection probability. This research provides educators and administrators with a theoretical framework and quantitative analytical tool for designing targeted information intervention strategies.

Keywords: idea propagation; recommendation algorithms; path modelling; student networks; trend analysis.

DOI: 10.1504/IJICT.2025.147760

International Journal of Information and Communication Technology, 2025 Vol.26 No.30, pp.113 - 127

Received: 04 Jun 2025
Accepted: 16 Jun 2025
Published online: 30 Jul 2025 *

Title: Modelling and trend analysis of student idea propagation paths facilitated by intelligent recommendation algorithms

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