{"title":"基于决策树的计算机自适应测试","authors":"M. Ueno, Pokpong Songmuang","doi":"10.1109/ICALT.2010.58","DOIUrl":null,"url":null,"abstract":"This paper proposes a new computerized adaptive testing employing a decision tree model, instead of test theories. The attribute variable of the model is examinees' responses to each item and the output variable is examinees' test total scores. Some simulation experiments show better performances of the proposed method compared to the traditional methods and solve the problems.","PeriodicalId":166491,"journal":{"name":"2010 10th IEEE International Conference on Advanced Learning Technologies","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Computerized Adaptive Testing Based on Decision Tree\",\"authors\":\"M. Ueno, Pokpong Songmuang\",\"doi\":\"10.1109/ICALT.2010.58\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a new computerized adaptive testing employing a decision tree model, instead of test theories. The attribute variable of the model is examinees' responses to each item and the output variable is examinees' test total scores. Some simulation experiments show better performances of the proposed method compared to the traditional methods and solve the problems.\",\"PeriodicalId\":166491,\"journal\":{\"name\":\"2010 10th IEEE International Conference on Advanced Learning Technologies\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 10th IEEE International Conference on Advanced Learning Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICALT.2010.58\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 10th IEEE International Conference on Advanced Learning Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALT.2010.58","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computerized Adaptive Testing Based on Decision Tree
This paper proposes a new computerized adaptive testing employing a decision tree model, instead of test theories. The attribute variable of the model is examinees' responses to each item and the output variable is examinees' test total scores. Some simulation experiments show better performances of the proposed method compared to the traditional methods and solve the problems.
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