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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >English teaching model and cultivation of students' speculative ability based on internet of things and typical case analysis
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English teaching model and cultivation of students' speculative ability based on internet of things and typical case analysis

机译:基于事物互联网的英语教学模式与学生投机能力的培养及典型案例分析

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摘要

The development of English teaching mode and students' speculative ability training mode is slow. In order to improve the quality of English teaching and improve students' speculative ability, the English teaching mode and the cultivation of students' speculative ability based on Internet of Things (IoT) are proposed and typical cases are analyzed. The computer data simulation model is used to construct an English teaching innovation model. The model proposes an optimization scheme from the algorithm flow, and uses the data transformation technology to improve the real-time teaching and strengthen the efficiency of teaching management. In order to further improve the students' speculative ability in English teaching, the particle swarm optimization algorithm is added to the model to realize the sequence optimization of the data, and the authenticity and efficiency of the particle swarm optimization algorithm in the English teaching model are verified. The test results show that the particle swarm algorithm can self-improve and repair functions, continuously improve the accuracy of the English teaching model, and optimize the teaching method. The research shows that the particle swarm optimization algorithm can improve the quality of English teaching and optimize the architecture, which can provide reference for the future integration of English teaching and computer technology.
机译:开发英语教学模式和学生投机能力训练模式缓慢。为了提高英语教学质量,提高学生的投机能力,提出了基于事物互联网(物联网)的英语教学模式和学生投机能力的培养,并分析了典型病例。计算机数据仿真模型用于构建英语教学创新模型。该模型提出了一种从算法流程的优化方案,并利用数据变换技术来改善实时教学并加强教学管理的效率。为了进一步提高学生的英语教学中的投机能力,将粒子群优化算法添加到模型中以实现数据的序列优化,以及英语教学模式中粒子群优化算法的真实性和效率验证。测试结果表明,粒子群算法可以自我提高和修复功能,不断提高英语教学模式的准确性,并优化教学方法。该研究表明,粒子群优化算法可以提高英语教学的质量,优化架构,可以为英语教学和计算机技术的未来集成提供参考。

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