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College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

机译：基于信息融合和优化RBF神经网络决策算法的大学英语教学质量评估系统

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摘要

In the process of deepening and developing the current higher education reform, people pay more and more attention to the research of college English education. The key to improve the college English education is to improve the quality of education, and learning evaluation is the key measure to improve the quality of education and training. This paper mainly studies the college English teaching quality evaluation system based on information fusion and optimized RBF neural network decision algorithm. This paper analyzes the main problems and complexity of creating an ideal learning quality evaluation system. On the basis of analyzing the advantages and disadvantages of the previous learning quality evaluation methods, this paper summarizes the existing learning quality evaluation methods and puts forward some suggestions according to the existing evaluation methods. A learning quality evaluation model based on RBF algorithm of neural network is proposed. RBF regularization network method, RBF neural network decision algorithm, and experimental investigation method are used to study the college English teaching quality evaluation system based on information fusion and optimization of RBF neural network decision algorithm. By innovating teaching methods and enriching teaching means, college students’ thirst for English knowledge can be aroused, and teachers’ teaching level can be improved. The results show that 50% of college students think that the level of college English teaching is average and needs to be improved. In the performance evaluation system of college English teaching quality based on information fusion and optimized RBF neural network decision algorithm, it is necessary to establish a learning evaluation system, monitor the learning quality in real time, find problems and improve them in time, and recognize the current situation of education.
机译：在深化和发展当前高等教育改革的过程中，人们越来越多地关注大学英语教育的研究。提高大学英语教育的关键是提高教育质量，学习评价是提高教育和培训质量的关键措施。本文主要研究了基于信息融合和优化RBF神经网络决策算法的大学英语教学质量评估系统。本文分析了创造理想学习质量评估系统的主要问题和复杂性。在分析先前学习质量评估方法的优缺点的基础上，本文总结了现有的学习质量评估方法，并根据现有的评估方法提出了一些建议。提出了一种基于神经网络RBF算法的学习质量评价模型。 RBF正则化网络方法，RBF神经网络决策算法和实验研究方法用于基于信息融合的大学英语教学质量评估系统，优化RBF神经网络决策算法。通过创新教学方法和丰富的教学方式，大学生可以引起英语知识的渴望，并且可以提高教师的教学水平。结果表明，50％的大学生认为大学英语教学水平是平均水平，需要改善。在基于信息融合和优化RBF神经网络决策算法的大学英语教学质量的性能评估系统中，有必要建立一个学习评估系统，实时监控学习质量，查找问题并及时改进它们，并识别目前的教育状况。

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  • 来源
    《Journal of Sensors》 |2021年第a期|共9页
  • 作者

    Yajun Chen;

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  • 中图分类 TP212;
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