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Risk Early Warning of a Dynamic Ideological and Political Education System Based on LSTM-MLP: Online Education Data Processing and Optimization.

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Title: Risk Early Warning of a Dynamic Ideological and Political Education System Based on LSTM-MLP: Online Education Data Processing and Optimization.
Authors: Zhan, Huan1 (AUTHOR), Meng, Xiangyun2 (AUTHOR), Asif, Muhammad3 (AUTHOR) Asif@ntu.edu.pk
Source: Mobile Networks & Applications. Apr2024, Vol. 29 Issue 2, p1-13. 13p.
Subjects: Computers in education, Online data processing, Online education, Political science education, Deep learning
Abstract: In online education, ensuring robust performance and preemptively addressing system vulnerabilities is crucial for enhancing user experience and operational efficiency. This study concentrates on developing a dynamic risk warning system for ideological and political education by utilizing LSTM-MLP models for the processing and optimization of online education data. The system encompasses functional modules designed from five distinct aspects: data collection, data analysis, early warning information presentation, intervention and effect evaluation, and system setup. By integrating LSTM for sequence learning and MLP for feature extraction and combining it with the static characteristics of students' behaviour portraits, the system effectively predicts and manages performance fluctuations and potential risks associated with varying user demands. In a test set including 800 participants, the proposed model achieves an accuracy of 0.9817 and a recall rate of 0.7633, significantly outperforming traditional models. Performance testing under irregular user increments demonstrates that the system maintains satisfactory response times and latency within acceptable thresholds, even under high concurrent loads. This research contributes to the resilience and reliability of online educational platforms, fostering improved user satisfaction and academic outcomes. [ABSTRACT FROM AUTHOR]
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.)
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  Data: In online education, ensuring robust performance and preemptively addressing system vulnerabilities is crucial for enhancing user experience and operational efficiency. This study concentrates on developing a dynamic risk warning system for ideological and political education by utilizing LSTM-MLP models for the processing and optimization of online education data. The system encompasses functional modules designed from five distinct aspects: data collection, data analysis, early warning information presentation, intervention and effect evaluation, and system setup. By integrating LSTM for sequence learning and MLP for feature extraction and combining it with the static characteristics of students' behaviour portraits, the system effectively predicts and manages performance fluctuations and potential risks associated with varying user demands. In a test set including 800 participants, the proposed model achieves an accuracy of 0.9817 and a recall rate of 0.7633, significantly outperforming traditional models. Performance testing under irregular user increments demonstrates that the system maintains satisfactory response times and latency within acceptable thresholds, even under high concurrent loads. This research contributes to the resilience and reliability of online educational platforms, fostering improved user satisfaction and academic outcomes. [ABSTRACT FROM AUTHOR]
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  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.)
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              Text: Apr2024
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