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<p>Sensor networks and deep learning technologies, as cutting-edge technologies, are increasingly widely applied in the field of education. Traditional online English education systems have problems such as high server pressure and poor teaching effectiveness, and there is an urgent need to enhance the reliability and teaching effectiveness of the system through technological innovation. This paper proposes an interaction model based on the SOA concept and the theory of cloud computing engineering, and applies it to the field of online English education, constructing an online English education model based on cloud services. Starting from the functional and non-functional requirements of the system, analyze the user roles, rationally allocate resources, and complete the functional design of cloud-based online English teaching. In system development, sensor networks are utilized to monitor students’ learning behaviors and teaching environments in real time, providing data support for personalized learning path planning and teaching decisions. Deep learning technology is applied to the realization of functions such as intelligent teaching content recommendation, speech recognition and pronunciation correction, and intelligent marking, enhancing teaching interactivity and learning outcomes. Through deep reinforcement learning algorithms, resource allocation is optimized to enhance the utilization rate and stability of system resources. The experimental results show that after introducing sensor networks and deep learning technologies, the resource utilization rate of the English online education system has been improved, the system response time has been shortened, and students’ learning participation and learning effects have been significantly enhanced. In cloud data center environments of different scales, the virtual machine placement algorithm based on multi-objective optimization (MO-VMP) can effectively reduce the load balancing factor and maintain the stability and efficiency of the system. By building an intelligent online English education system, new ideas and methods have been provided for the development of educational informatization, promoting the development of online English education towards intelligence and personalization.</p>

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Reliability analysis of English online education based on sensor networks and deep learning

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Wei Lin,
  • Yixin Gao

摘要

Sensor networks and deep learning technologies, as cutting-edge technologies, are increasingly widely applied in the field of education. Traditional online English education systems have problems such as high server pressure and poor teaching effectiveness, and there is an urgent need to enhance the reliability and teaching effectiveness of the system through technological innovation. This paper proposes an interaction model based on the SOA concept and the theory of cloud computing engineering, and applies it to the field of online English education, constructing an online English education model based on cloud services. Starting from the functional and non-functional requirements of the system, analyze the user roles, rationally allocate resources, and complete the functional design of cloud-based online English teaching. In system development, sensor networks are utilized to monitor students’ learning behaviors and teaching environments in real time, providing data support for personalized learning path planning and teaching decisions. Deep learning technology is applied to the realization of functions such as intelligent teaching content recommendation, speech recognition and pronunciation correction, and intelligent marking, enhancing teaching interactivity and learning outcomes. Through deep reinforcement learning algorithms, resource allocation is optimized to enhance the utilization rate and stability of system resources. The experimental results show that after introducing sensor networks and deep learning technologies, the resource utilization rate of the English online education system has been improved, the system response time has been shortened, and students’ learning participation and learning effects have been significantly enhanced. In cloud data center environments of different scales, the virtual machine placement algorithm based on multi-objective optimization (MO-VMP) can effectively reduce the load balancing factor and maintain the stability and efficiency of the system. By building an intelligent online English education system, new ideas and methods have been provided for the development of educational informatization, promoting the development of online English education towards intelligence and personalization.