Article: Enhancing English language teaching quality evaluation via dynamic multimodal cognitive transfer models Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.32 pp.121 - 141 Abstract: This paper proposes a dynamic assessment method based on multimodal cognitive transfer modelling to address the limitations of static and unidimensional analysis in English teaching quality assessment. A two-channel LSTM-cognitive state space model is constructed by synchronously collecting four-dimensional data of speech, vision, text and physiological signals in the teaching scene, quantifying students' cognitive state transfer trajectories based on the ACT-R cognitive architecture in the knowledge transfer channel, and adopting a dynamic causal map to model the feedback mechanism of teachers' strategy adjustment in the teaching intervention channel. A time-varying weighted assessment function was designed to dynamically fuse cognitive state vectors with intervention intensity. In a 136-lesson experiment in 12 schools, the causal attribution rate of this method was improved by 41.2%, and the adoption rate of intervention suggestions reached 83.7%, which verified the effectiveness and universality for dynamic quality assessment of English teaching. Inderscience Publishers - linking academia, business and industry through research

Title: Enhancing English language teaching quality evaluation via dynamic multimodal cognitive transfer models

Authors: Changying Yan

Addresses: Education Department, Chizhou Vocational and Technical College, Chizhou 247000, China

Abstract: This paper proposes a dynamic assessment method based on multimodal cognitive transfer modelling to address the limitations of static and unidimensional analysis in English teaching quality assessment. A two-channel LSTM-cognitive state space model is constructed by synchronously collecting four-dimensional data of speech, vision, text and physiological signals in the teaching scene, quantifying students' cognitive state transfer trajectories based on the ACT-R cognitive architecture in the knowledge transfer channel, and adopting a dynamic causal map to model the feedback mechanism of teachers' strategy adjustment in the teaching intervention channel. A time-varying weighted assessment function was designed to dynamically fuse cognitive state vectors with intervention intensity. In a 136-lesson experiment in 12 schools, the causal attribution rate of this method was improved by 41.2%, and the adoption rate of intervention suggestions reached 83.7%, which verified the effectiveness and universality for dynamic quality assessment of English teaching.

Keywords: multimodal cognitive transfer modelling; dynamic assessment; cross-modal fusion; cognitive state space models.

DOI: 10.1504/IJICT.2025.148497

International Journal of Information and Communication Technology, 2025 Vol.26 No.32, pp.121 - 141

Received: 26 Jun 2025
Accepted: 18 Jul 2025
Published online: 08 Sep 2025 *

Title: Enhancing English language teaching quality evaluation via dynamic multimodal cognitive transfer models

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