基于粒子群算法的大学英语教学质量评价

Kanghua Gao
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引用次数: 2

摘要

教学质量评价是教学管理的重要组成部分。它是提高教学质量和学校运行效率的重要工具。开展以教师课堂教学为对象,以学生、专家、同行为主体的质量评价是实施质量评价的关键环节。本文旨在研究基于粒子群优先算法的大学英语教学质量评价。基于粒子优化算法,将影响高校教学质量的因素分为“因果”两类。在对整个系统进行评价的基础上,结合层次分析法的应用,根据各因素对系统的影响状态和影响程度逐层修正。权重可以降低统计平均法的主观水平;同时,教学质量是一个综合性指标,引入评价指标的概念为高校教学质量评价提供了新的思路。实验结果表明,本文引入了粒子群优化算法对其进行了改进。通过实验和比较,证明所构建的模型对教学质量评价是准确的,对改进教学管理具有良好的现实意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of College English Teaching Quality Based on Particle Swarm Optimization Algorithm
Teaching quality evaluation is an important part of teaching management. It is an important tool to improve teaching quality and school operation efficiency. Carrying out quality evaluation with teachers' classroom teaching as the object and students, experts and peers as the main body is the key link in the implementation of quality evaluation. This paper aims to study the evaluation of college English teaching quality based on particle swarm first algorithm. Based on the particle optimization algorithm, this paper divides the factors that affect the quality of university teaching into "causal" two types. Based on the evaluation of the entire system, combined with the application of analytic hierarchy process, various factors are revised layer by layer according to the status and degree of influence on the system. The weight can reduce the subjective level of the statistical average method; at the same time, the quality of teaching is a Comprehensive indicators, introducing the concept of evaluation indicators to provide new ideas for college teaching quality evaluation. The experimental results of this paper show that this paper introduces the particle swarm optimization algorithm to improve it. Through experiments and comparisons, it proves that the constructed model is accurate for teaching quality evaluation and has good practical significance in improving teaching management.
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