Article: Analysis of EFL learners' academic anxiety based on R language and graph neural network Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.26 pp.1 - 16 Abstract: The academic anxiety of English as a foreign language (EFL) learners has progressively become a major determinant of academic performance. Therefore, how best to forecast and evaluate the influence of these emotions on academic performance has become a hot issue of research in academia. This work suggests an academic anxiety emotion analysis based on graph neural network (GNN). This work uses the GNN model to investigate learners' anxiety feelings in depth by processing multimodal data on their emotional traits, therefore verifying the accuracy of the approach in forecasting academic anxiety emotions. According to the experimental results, the suggested approach has tremendous generalisation capacity and application possibilities and greatly beats conventional machine learning methods in many respects. This paper offers fresh perspectives on the academic anxiety and theoretical support for the design of emotional control techniques in education of EFL learners. Inderscience Publishers - linking academia, business and industry through research

Title: Analysis of EFL learners' academic anxiety based on R language and graph neural network

Authors: Ruyi Jin

Addresses: Department of Humanities and Tourism, Hohhot Vocational College, Hohhot, 010070, China

Abstract: The academic anxiety of English as a foreign language (EFL) learners has progressively become a major determinant of academic performance. Therefore, how best to forecast and evaluate the influence of these emotions on academic performance has become a hot issue of research in academia. This work suggests an academic anxiety emotion analysis based on graph neural network (GNN). This work uses the GNN model to investigate learners' anxiety feelings in depth by processing multimodal data on their emotional traits, therefore verifying the accuracy of the approach in forecasting academic anxiety emotions. According to the experimental results, the suggested approach has tremendous generalisation capacity and application possibilities and greatly beats conventional machine learning methods in many respects. This paper offers fresh perspectives on the academic anxiety and theoretical support for the design of emotional control techniques in education of EFL learners.

Keywords: graph neural network; GNN; academic anxiety; EFL learners; multimodal data; sentiment prediction; sentiment analysis.

DOI: 10.1504/IJICT.2025.147466

International Journal of Information and Communication Technology, 2025 Vol.26 No.26, pp.1 - 16

Received: 06 May 2025
Accepted: 23 May 2025
Published online: 16 Jul 2025 *

Title: Analysis of EFL learners' academic anxiety based on R language and graph neural network

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