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<p>In cloud computing environments, data storage, processing, and transmission face multiple security threats such as data breaches, malicious attacks, and service disruptions. These challenges not only hinder the advancement of educational informatization but also pose severe risks to the security and reliability of online evaluation systems in ideological and political education classrooms. Against this backdrop, this paper explores the application of sensor network technology with cloud computing network security and behavioral pattern recognition in evaluating online classroom participation. By employing sensor networks to collect behavioral data from students during lectures—including seat adjustment frequency, blink rate, and head posture changes—this approach analyzes these patterns to assess attention levels and engagement. Regarding cloud computing network security, the study proposes multiple protective measures including data encryption, identity authentication, access control, firewall deployment, and intrusion detection system implementation to ensure data security and system stability. Experimental comparisons evaluate different behavioral pattern recognition algorithms in terms of accuracy and computational efficiency, while independent samples t-tests analyze online learning duration and exam scores between experimental and control classes. Results demonstrate that the cloud-based sensor network with behavioral pattern recognition effectively monitors student behavior patterns, provides teachers with timely and objective feedback, and requires relatively lower training time. The t-test analysis reveals that experimental class students outperformed control group members in both online learning duration and exam scores, indicating this evaluation method significantly enhances learning outcomes and teaching quality. By ensuring the security of data collection and processing, as well as providing reliable technical support for the evaluation system, this method can objectively and scientifically evaluate the teaching effect of classroom teaching, provide a strong basis for teachers to adjust teaching strategies and improve teaching quality, and also provide personalized guidance for students ‘learning, which is helpful to enhance students’ learning enthusiasm and academic performance.</p>

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Cloud computing network security based on sensor networks and behavior pattern recognition promotes online evaluation of political classrooms

  • 期刊论文

  • 发表日期：

    2026年4月2日

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  • Chenxi Wang

摘要

In cloud computing environments, data storage, processing, and transmission face multiple security threats such as data breaches, malicious attacks, and service disruptions. These challenges not only hinder the advancement of educational informatization but also pose severe risks to the security and reliability of online evaluation systems in ideological and political education classrooms. Against this backdrop, this paper explores the application of sensor network technology with cloud computing network security and behavioral pattern recognition in evaluating online classroom participation. By employing sensor networks to collect behavioral data from students during lectures—including seat adjustment frequency, blink rate, and head posture changes—this approach analyzes these patterns to assess attention levels and engagement. Regarding cloud computing network security, the study proposes multiple protective measures including data encryption, identity authentication, access control, firewall deployment, and intrusion detection system implementation to ensure data security and system stability. Experimental comparisons evaluate different behavioral pattern recognition algorithms in terms of accuracy and computational efficiency, while independent samples t-tests analyze online learning duration and exam scores between experimental and control classes. Results demonstrate that the cloud-based sensor network with behavioral pattern recognition effectively monitors student behavior patterns, provides teachers with timely and objective feedback, and requires relatively lower training time. The t-test analysis reveals that experimental class students outperformed control group members in both online learning duration and exam scores, indicating this evaluation method significantly enhances learning outcomes and teaching quality. By ensuring the security of data collection and processing, as well as providing reliable technical support for the evaluation system, this method can objectively and scientifically evaluate the teaching effect of classroom teaching, provide a strong basis for teachers to adjust teaching strategies and improve teaching quality, and also provide personalized guidance for students ‘learning, which is helpful to enhance students’ learning enthusiasm and academic performance.