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A Multimodal Interaction Mechanism for Mobile English Learning in Higher Education.

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Title: A Multimodal Interaction Mechanism for Mobile English Learning in Higher Education.
Authors: Zhao, Ran1 zhaoran118@163.com, Dong, Ning1 dongning000521@126.com
Source: International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 16, p134-148. 15p.
Subjects: Mobile learning, Multisensor data fusion, Synchronization, Artificial neural networks, Higher education, Machine learning, Multimodal user interfaces
Abstract: Sensor-enabled mobile devices have provided new opportunities for interaction analysis and cognitive assessment in mobile English learning within higher education. However, existing approaches are constrained by insufficient data synchronization accuracy, limited flexibility in multimodal feature fusion, high computational costs associated with sequence modeling, and the lack of effective intervention mechanisms. To address these limitations, an edgeside multimodal interaction analysis and adaptive intervention framework was developed. Temporal alignment of heterogeneous sensor data was achieved, while lightweight crossmodal feature fusion was implemented using channel-attention-based representations. A bidirectional Mamba architecture was introduced for processing long-sequence data, and multitask learning was employed to simultaneously evaluate interaction quality and cognitive load. Behavioral evolution patterns were further explored through sequential pattern mining and cognitive network analysis. On this basis, a hierarchical feedback strategy was designed to enable intelligent intervention. The proposed framework provides both theoretical foundations and technical support for the development of intelligent mobile educational applications. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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
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  Data: A Multimodal Interaction Mechanism for Mobile English Learning in Higher Education.
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  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Ran%22">Zhao, Ran</searchLink><relatesTo>1</relatesTo><i> zhaoran118@163.com</i><br /><searchLink fieldCode="AR" term="%22Dong%2C+Ning%22">Dong, Ning</searchLink><relatesTo>1</relatesTo><i> dongning000521@126.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 16, p134-148. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Synchronization%22">Synchronization</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multimodal+user+interfaces%22">Multimodal user interfaces</searchLink>
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  Data: Sensor-enabled mobile devices have provided new opportunities for interaction analysis and cognitive assessment in mobile English learning within higher education. However, existing approaches are constrained by insufficient data synchronization accuracy, limited flexibility in multimodal feature fusion, high computational costs associated with sequence modeling, and the lack of effective intervention mechanisms. To address these limitations, an edgeside multimodal interaction analysis and adaptive intervention framework was developed. Temporal alignment of heterogeneous sensor data was achieved, while lightweight crossmodal feature fusion was implemented using channel-attention-based representations. A bidirectional Mamba architecture was introduced for processing long-sequence data, and multitask learning was employed to simultaneously evaluate interaction quality and cognitive load. Behavioral evolution patterns were further explored through sequential pattern mining and cognitive network analysis. On this basis, a hierarchical feedback strategy was designed to enable intelligent intervention. The proposed framework provides both theoretical foundations and technical support for the development of intelligent mobile educational applications. [ABSTRACT FROM AUTHOR]
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  Group: Ab
  Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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.)
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        Value: 10.3991/ijim.v20i16.62751
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        Text: English
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        StartPage: 134
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      – SubjectFull: Mobile learning
        Type: general
      – SubjectFull: Multisensor data fusion
        Type: general
      – SubjectFull: Synchronization
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Higher education
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      – SubjectFull: Machine learning
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      – SubjectFull: Multimodal user interfaces
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              Text: 2026
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