{"title":"基于粒子群算法的大学英语教学质量评价","authors":"Kanghua Gao","doi":"10.1145/3448734.3450831","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":105999,"journal":{"name":"The 2nd International Conference on Computing and Data Science","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Evaluation of College English Teaching Quality Based on Particle Swarm Optimization Algorithm\",\"authors\":\"Kanghua Gao\",\"doi\":\"10.1145/3448734.3450831\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":105999,\"journal\":{\"name\":\"The 2nd International Conference on Computing and Data Science\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2nd International Conference on Computing and Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3448734.3450831\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2nd International Conference on Computing and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3448734.3450831","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.