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Service Enhancement and System Maintenance for MEIS.

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Title: Service Enhancement and System Maintenance for MEIS.
Authors: Li, Caiyi1,2 (AUTHOR), Peng, Changling1,2 (AUTHOR), Liu, Shuai1,2 (AUTHOR) liushuai@hunnu.edu.cn
Source: Mobile Networks & Applications. Dec2025, Vol. 30 Issue 5/6, p1005-1007. 3p.
Subjects: Recommender systems, Course evaluation (Education), Intelligent tutoring systems, Computational intelligence, 5G networks, Data protection, Data mining, Reliability in engineering
Abstract: This article focuses on advancements and challenges in mobile education intelligent systems (MEIS) amid the rise of digital intelligence and 5G technology. It highlights recent research efforts to enhance MEIS services through personalized recommendation algorithms, learning behavior mining, course quality assessment, and action correction technologies, aiming to improve user experience and educational quality. Additionally, it addresses performance maintenance strategies including information sharing, risk warning systems, hierarchical data storage, and fake news detection to ensure system reliability and user privacy protection. The article emphasizes the need for interdisciplinary collaboration to further develop MEIS with autonomous learning and adaptive capabilities in the evolving digital education landscape. [Extracted from the article]
Copyright of Mobile Networks & Applications is the property of Springer Nature 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: Service Enhancement and System Maintenance for MEIS.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Caiyi%22">Li, Caiyi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Peng%2C+Changling%22">Peng, Changling</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Shuai%22">Liu, Shuai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liushuai@hunnu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Mobile+Networks+%26+Applications%22">Mobile Networks & Applications</searchLink>. Dec2025, Vol. 30 Issue 5/6, p1005-1007. 3p.
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  Data: <searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Course+evaluation+%28Education%29%22">Course evaluation (Education)</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+intelligence%22">Computational intelligence</searchLink><br /><searchLink fieldCode="DE" term="%225G+networks%22">5G networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+protection%22">Data protection</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability+in+engineering%22">Reliability in engineering</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This article focuses on advancements and challenges in mobile education intelligent systems (MEIS) amid the rise of digital intelligence and 5G technology. It highlights recent research efforts to enhance MEIS services through personalized recommendation algorithms, learning behavior mining, course quality assessment, and action correction technologies, aiming to improve user experience and educational quality. Additionally, it addresses performance maintenance strategies including information sharing, risk warning systems, hierarchical data storage, and fake news detection to ensure system reliability and user privacy protection. The article emphasizes the need for interdisciplinary collaboration to further develop MEIS with autonomous learning and adaptive capabilities in the evolving digital education landscape. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Mobile Networks & Applications is the property of Springer Nature 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=194576087
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      – Type: doi
        Value: 10.1007/s11036-025-02440-1
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        Text: English
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      – SubjectFull: Recommender systems
        Type: general
      – SubjectFull: Course evaluation (Education)
        Type: general
      – SubjectFull: Intelligent tutoring systems
        Type: general
      – SubjectFull: Computational intelligence
        Type: general
      – SubjectFull: 5G networks
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      – SubjectFull: Data protection
        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Reliability in engineering
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      – TitleFull: Service Enhancement and System Maintenance for MEIS.
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            NameFull: Li, Caiyi
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            NameFull: Peng, Changling
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            NameFull: Liu, Shuai
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            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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              Value: 5/6
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