{"title":"基于主成分分析和支持向量机的《数据结构》课程教学质量评价","authors":"Xu Xin-ai, Wang Li-na, Qi Chun-ying","doi":"10.1109/ICCEAI52939.2021.00085","DOIUrl":null,"url":null,"abstract":"In order to obtain a higher-precision “data structure” teaching quality evaluation result, according to the characteristics of the “data structure” teaching quality evaluation. A “data structure” teaching quality evaluation model based on the combination of principal component analysis and support vector machine is designed and applied to specific examples. The experimental results confirm that the established model has obtained high-precision evaluation results in the “data structure” teaching quality evaluation level, which can provide valuable information for improving the teaching quality evaluation of “data structure” and related courses.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Teaching Quality Evaluation of “Data Structure” Courses Based on Principal Component Analysis and Support Vector Machine\",\"authors\":\"Xu Xin-ai, Wang Li-na, Qi Chun-ying\",\"doi\":\"10.1109/ICCEAI52939.2021.00085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to obtain a higher-precision “data structure” teaching quality evaluation result, according to the characteristics of the “data structure” teaching quality evaluation. A “data structure” teaching quality evaluation model based on the combination of principal component analysis and support vector machine is designed and applied to specific examples. The experimental results confirm that the established model has obtained high-precision evaluation results in the “data structure” teaching quality evaluation level, which can provide valuable information for improving the teaching quality evaluation of “data structure” and related courses.\",\"PeriodicalId\":331409,\"journal\":{\"name\":\"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEAI52939.2021.00085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEAI52939.2021.00085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Teaching Quality Evaluation of “Data Structure” Courses Based on Principal Component Analysis and Support Vector Machine
In order to obtain a higher-precision “data structure” teaching quality evaluation result, according to the characteristics of the “data structure” teaching quality evaluation. A “data structure” teaching quality evaluation model based on the combination of principal component analysis and support vector machine is designed and applied to specific examples. The experimental results confirm that the established model has obtained high-precision evaluation results in the “data structure” teaching quality evaluation level, which can provide valuable information for improving the teaching quality evaluation of “data structure” and related courses.