Article: Educational resource allocation optimisation driven by multimodal feature fusion Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.31 pp.105 - 125 Abstract: As information technology and artificial intelligence grow quickly, intelligent transformation in education has become a major trend. This study suggests an intelligent educational resource allocation model based on multimodal feature fusion-driven (MERA) to fix the flaws with the current system, which are that it is static, not very responsive, and not very personalised. MERA combines the transformer structure, self-attention mechanism, and graph neural network (GNN) with multi-objective optimisation strategies to provide a detailed model and dynamic resource allocation for complicated, varied educational data. To fully test the model's performance, three related experiments are planned and carried out. The results reveal that the MERA model is far better at using resources efficiently. In general, this study gives intelligent educational resource management a new technical path and a theoretical base. Inderscience Publishers - linking academia, business and industry through research

Title: Educational resource allocation optimisation driven by multimodal feature fusion

Authors: Junhua Hao

Addresses: School of Business, Anyang Institute of Technology, Anyang 455000, China

Abstract: As information technology and artificial intelligence grow quickly, intelligent transformation in education has become a major trend. This study suggests an intelligent educational resource allocation model based on multimodal feature fusion-driven (MERA) to fix the flaws with the current system, which are that it is static, not very responsive, and not very personalised. MERA combines the transformer structure, self-attention mechanism, and graph neural network (GNN) with multi-objective optimisation strategies to provide a detailed model and dynamic resource allocation for complicated, varied educational data. To fully test the model's performance, three related experiments are planned and carried out. The results reveal that the MERA model is far better at using resources efficiently. In general, this study gives intelligent educational resource management a new technical path and a theoretical base.

Keywords: multimodal feature fusion; intelligent educational resource allocation; transformer; graph neural network; GNN; multi-objective optimisation.

DOI: 10.1504/IJICT.2025.148129

International Journal of Information and Communication Technology, 2025 Vol.26 No.31, pp.105 - 125

Received: 17 Jun 2025
Accepted: 08 Jul 2025
Published online: 26 Aug 2025 *

Title: Educational resource allocation optimisation driven by multimodal feature fusion

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