Roberto Jaunez, Jorge J. Villalón, Gonzalo Muñoz, Gabriela Baez
{"title":"一种基于评分者个体差异预测写作评估中评分者一致性的方法","authors":"Roberto Jaunez, Jorge J. Villalón, Gonzalo Muñoz, Gabriela Baez","doi":"10.1109/ICALT.2019.00083","DOIUrl":null,"url":null,"abstract":"Writing has been increasingly assessed within educational scenarios. However, assessing writing is a highly subjective task, making the grading of such tests a costly process that demands several graders for calculating interrater agreement to ensure reliability. A drawback of this approach is that interrater agreement does not explain the source of markers' differences; therefore, when agreement is low, the marking process must be repeated, and markers retrained. This article proposes a method to predict agreement, based on markers' individual differences using item response theory. Results showed that the method accurately predicts agreement even with partial evidence.","PeriodicalId":356549,"journal":{"name":"2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Method to Predict Interrater Agreement in Writing Assessment Based on Raters' Individual Differences\",\"authors\":\"Roberto Jaunez, Jorge J. Villalón, Gonzalo Muñoz, Gabriela Baez\",\"doi\":\"10.1109/ICALT.2019.00083\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Writing has been increasingly assessed within educational scenarios. However, assessing writing is a highly subjective task, making the grading of such tests a costly process that demands several graders for calculating interrater agreement to ensure reliability. A drawback of this approach is that interrater agreement does not explain the source of markers' differences; therefore, when agreement is low, the marking process must be repeated, and markers retrained. This article proposes a method to predict agreement, based on markers' individual differences using item response theory. Results showed that the method accurately predicts agreement even with partial evidence.\",\"PeriodicalId\":356549,\"journal\":{\"name\":\"2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICALT.2019.00083\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALT.2019.00083","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Method to Predict Interrater Agreement in Writing Assessment Based on Raters' Individual Differences
Writing has been increasingly assessed within educational scenarios. However, assessing writing is a highly subjective task, making the grading of such tests a costly process that demands several graders for calculating interrater agreement to ensure reliability. A drawback of this approach is that interrater agreement does not explain the source of markers' differences; therefore, when agreement is low, the marking process must be repeated, and markers retrained. This article proposes a method to predict agreement, based on markers' individual differences using item response theory. Results showed that the method accurately predicts agreement even with partial evidence.