{"title":"Analyzing the quality evaluation of college English teaching based on probabilistic linguistic multiple-attribute group decision-making","authors":"Ruoxi Hu , Qingmao Wang","doi":"10.1016/j.sasc.2025.200191","DOIUrl":null,"url":null,"abstract":"<div><div>The evaluation of college English teaching quality aims to comprehensively assess the achievement of teaching objectives and effectiveness through the analysis and feedback on teachers' teaching abilities, course design, and students' learning outcomes. The evaluation combines both quantitative and qualitative methods, focusing not only on the scientific and practical aspects of teaching content but also on the improvement of students' language proficiency and overall development. A scientific evaluation system encourages teachers to refine their teaching methods, enhances teaching efficiency, and provides data support for curriculum optimization, thereby continuously improving the quality of college English teaching to meet students' academic and career development needs. The quality evaluation of college English teaching is multiple-attribute group decision-making (MAGDM). To address this, combined TODIM (Logarithmic TODIM and Exponential TODIM) and PROMETHEE approaches are utilized to propose a MAGDM framework. Considering the need to capture fuzzy information during the quality evaluation process, probabilistic linguistic term sets (PLTSs) are employed. In this study, we construct the probabilistic linguistic combined TODIM-PROMETHEE (PL-Com-TODIM-PROMETHEE) approach to tackle MAGDM under PLTSs. To determine the weight values within the PLTSs framework, we employ the MEREC approach. Finally, a numerical example is presented to validate the effectiveness of the PL-Com-TODIM-PROMETHEE approach for quality evaluation of college English teaching. Through this approach, the study contributes to the advancement of quality evaluation methodologies by integrating combined TODIM and PROMETHEE within the PLTSs framework. It addresses the challenges posed by fuzzy information and provides a practical and effective approach for decision-making in the context of quality evaluation of college English teaching.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200191"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems and Soft Computing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772941925000092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The evaluation of college English teaching quality aims to comprehensively assess the achievement of teaching objectives and effectiveness through the analysis and feedback on teachers' teaching abilities, course design, and students' learning outcomes. The evaluation combines both quantitative and qualitative methods, focusing not only on the scientific and practical aspects of teaching content but also on the improvement of students' language proficiency and overall development. A scientific evaluation system encourages teachers to refine their teaching methods, enhances teaching efficiency, and provides data support for curriculum optimization, thereby continuously improving the quality of college English teaching to meet students' academic and career development needs. The quality evaluation of college English teaching is multiple-attribute group decision-making (MAGDM). To address this, combined TODIM (Logarithmic TODIM and Exponential TODIM) and PROMETHEE approaches are utilized to propose a MAGDM framework. Considering the need to capture fuzzy information during the quality evaluation process, probabilistic linguistic term sets (PLTSs) are employed. In this study, we construct the probabilistic linguistic combined TODIM-PROMETHEE (PL-Com-TODIM-PROMETHEE) approach to tackle MAGDM under PLTSs. To determine the weight values within the PLTSs framework, we employ the MEREC approach. Finally, a numerical example is presented to validate the effectiveness of the PL-Com-TODIM-PROMETHEE approach for quality evaluation of college English teaching. Through this approach, the study contributes to the advancement of quality evaluation methodologies by integrating combined TODIM and PROMETHEE within the PLTSs framework. It addresses the challenges posed by fuzzy information and provides a practical and effective approach for decision-making in the context of quality evaluation of college English teaching.