Predicting Learners' Attention Under Audiovisual Cues in Virtual Reality With a Deep Learning Model

ABSTRACT Effective audiovisual cueing can significantly enhance learners' attention to educational resources in the Virtual Reality (VR). However, predicting the impact of multimodal cueing on learners' attention in immersive teaching environments remains a challenging task. To address this, we propose a deep learning model named Attention Prediction Model (APM). This model employs RevFCN to extract visual and auditory cue features and incorporates a tailored Upsample‐Aggregation Fusion Module (UAFM) to integrate multimodal representations. Additionally, an SANet is introduced to effectively combine the advantages of spatial and channel attention. Trained on our constructed dataset, APM achieved an attention prediction accuracy of 81.6%. These findings offer both theoretical and practical implications for the application of multimodal cueing in VR‐based instructional design.