Article: Construction of multimodal perception and quantitative assessment model of interactive participation in English classroom Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.42 pp.1 - 19 Abstract: AI enhances classroom behaviour analysis, yet participation assessment remains subjective, single-dimensional, and lacks real-time quantitative support. This study proposes a multimodal framework integrating video, audio, eye tracking, and positioning to capture facial expressions, body posture, speech emotion, and semantic content. A dual-stream ResNet, TCN, and pretrained transformer are employed to extract visual, acoustic, and textual features, with cross-modal alignment achieved via timestamp synchronisation and learnable positional encoding. A multi-head self-attention-based fusion module and multi-task evaluation head quantify participation frequency, interaction depth, emotional engagement, and knowledge feedback. Experiments on 192 students across 90 classes achieve 91.2% accuracy, over 90% F1-score, and over 93% consistency with teacher ratings, significantly outperforming baseline methods. Inderscience Publishers - linking academia, business and industry through research

Title: Construction of multimodal perception and quantitative assessment model of interactive participation in English classroom

Authors: Guanguan Zeng; Junge Liu

Addresses: The School of Foreign Languages, Shanghai Zhongqiao Vocational and Technical University, Shanghai, 201514, China ' The School of Foreign Languages, Shanghai Zhongqiao Vocational and Technical University, Shanghai, 201514, China

Abstract: AI enhances classroom behaviour analysis, yet participation assessment remains subjective, single-dimensional, and lacks real-time quantitative support. This study proposes a multimodal framework integrating video, audio, eye tracking, and positioning to capture facial expressions, body posture, speech emotion, and semantic content. A dual-stream ResNet, TCN, and pretrained transformer are employed to extract visual, acoustic, and textual features, with cross-modal alignment achieved via timestamp synchronisation and learnable positional encoding. A multi-head self-attention-based fusion module and multi-task evaluation head quantify participation frequency, interaction depth, emotional engagement, and knowledge feedback. Experiments on 192 students across 90 classes achieve 91.2% accuracy, over 90% F1-score, and over 93% consistency with teacher ratings, significantly outperforming baseline methods.

Keywords: multimodal perception; classroom participation; English teaching; deep learning.

DOI: 10.1504/IJICT.2026.153379

International Journal of Information and Communication Technology, 2026 Vol.27 No.42, pp.1 - 19

Received: 29 Aug 2025
Accepted: 19 Sep 2025
Published online: 06 May 2026 *

Title: Construction of multimodal perception and quantitative assessment model of interactive participation in English classroom

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