Online education and remote meetings make it difficult for speakers to gauge audience attention in real time; without direct eye contact and interactive feedback

communication effectiveness often suffers. Studies show that in face-to-face communication only about 7% of information comes from verbal content

38% from voice tone

and as much as 55% comes from facial expressions and body language (nonverbal cues). Therefore

a vision-based model that can assess audience attentiveness via visual signals without requiring wearables is highly valuable. Building on prior work

this paper systematically reviews key background and methods

and proposes a temporal multi-modal model fusing facial expression and body posture features. We train and evaluate the model on public datasets such as DAiSEE and on self-recorded TED-style presentation videos. The fusion model achieves 86.3% accuracy and a macro-average F1 score of 0.84 in a four-class attention classification task

significantly outperforming a baseline model. Additionally

extensive experiments validate the model's robustness and applicability. Finally

we discuss potential application scenarios

ethical/privacy issues

and future development trends for attention detection technology in smart education and human–computer interaction.