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Teaching Strategies for English Audio-Visual Oral Instruction Based on Deep Neural Networks.

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Title: Teaching Strategies for English Audio-Visual Oral Instruction Based on Deep Neural Networks.
Authors: Xu, Tianshu1 (AUTHOR) xuts1117@163.com, Zhang, Peilin2 (AUTHOR), Yin, Jingjing2 (AUTHOR)
Source: International Journal of High Speed Electronics & Systems. Sep2026, Vol. 35 Issue 4, p1-42. 42p.
Subjects: Artificial neural networks, English language education, Audiovisual education, Instructional systems, Individualized instruction, Cognitive learning, Linguistics education, Teaching methods
Abstract: With the rapid evolution of educational technology, leveraging advanced methodologies for English instruction has become increasingly critical to addressing the growing demand for efficient and engaging language learning. Current approaches in English language teaching often fall short in personalization, adaptability, and learner engagement, primarily due to their static structure and limited integration of cognitive and technological advancements. To bridge this gap, we propose a novel framework grounded in deep neural networks to enhance English Audio Visual Oral (AVO) instruction, aligning with the thematic scope of computational advancements in education. This study introduces the Adaptive Cognitive Learning Model (ACLM), a pedagogical innovation designed to dynamically adjust teaching strategies to individual learner profiles by integrating real-time performance feedback, modular content delivery, and multimedia-assisted learning. The ACLM employs a systematic feedback loop and adaptive mechanisms to personalize learning pathways, addressing domain-specific challenges such as vocabulary acquisition, grammar comprehension, and conversational fluency. Experimental evaluations demonstrate that our method significantly improves learner outcomes in engagement, comprehension, and retention compared to traditional approaches. These findings underscore the potential of combining cognitive alignment with dynamic neural networks to establish scalable, personalized, and effective instructional strategies in English AVO education, contributing to advancements in computational language pedagogy. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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DbLabel: Engineering Source
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  Data: Teaching Strategies for English Audio-Visual Oral Instruction Based on Deep Neural Networks.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22English+language+education%22">English language education</searchLink><br /><searchLink fieldCode="DE" term="%22Audiovisual+education%22">Audiovisual education</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+instruction%22">Individualized instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+learning%22">Cognitive learning</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistics+education%22">Linguistics education</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+methods%22">Teaching methods</searchLink>
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  Data: With the rapid evolution of educational technology, leveraging advanced methodologies for English instruction has become increasingly critical to addressing the growing demand for efficient and engaging language learning. Current approaches in English language teaching often fall short in personalization, adaptability, and learner engagement, primarily due to their static structure and limited integration of cognitive and technological advancements. To bridge this gap, we propose a novel framework grounded in deep neural networks to enhance English Audio Visual Oral (AVO) instruction, aligning with the thematic scope of computational advancements in education. This study introduces the Adaptive Cognitive Learning Model (ACLM), a pedagogical innovation designed to dynamically adjust teaching strategies to individual learner profiles by integrating real-time performance feedback, modular content delivery, and multimedia-assisted learning. The ACLM employs a systematic feedback loop and adaptive mechanisms to personalize learning pathways, addressing domain-specific challenges such as vocabulary acquisition, grammar comprehension, and conversational fluency. Experimental evaluations demonstrate that our method significantly improves learner outcomes in engagement, comprehension, and retention compared to traditional approaches. These findings underscore the potential of combining cognitive alignment with dynamic neural networks to establish scalable, personalized, and effective instructional strategies in English AVO education, contributing to advancements in computational language pedagogy. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190716994
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        Value: 10.1142/S0129156425404565
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      – Code: eng
        Text: English
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      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: English language education
        Type: general
      – SubjectFull: Audiovisual education
        Type: general
      – SubjectFull: Instructional systems
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      – SubjectFull: Individualized instruction
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      – SubjectFull: Cognitive learning
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      – SubjectFull: Linguistics education
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      – SubjectFull: Teaching methods
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      – TitleFull: Teaching Strategies for English Audio-Visual Oral Instruction Based on Deep Neural Networks.
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            NameFull: Xu, Tianshu
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              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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