{"title":"基于贝叶斯网络的计算机自适应测试与学习","authors":"K. Kim, Y. Choi","doi":"10.1145/2451176.2451215","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel CAT (Computerized Adaptive Testing) system based on Bayesian network. Our novel system makes good use of topology and probabilistic inference algorithm of Bayesian network to efficiently estimate proficiency of learner and also give an adaptive learning guide when needed. From several experiments, we identified that our system could considerably improve proficiency-estimation performance when compared with conventional CAT methods.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"109 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Computerized adaptive testing and learning using bayesian network\",\"authors\":\"K. Kim, Y. Choi\",\"doi\":\"10.1145/2451176.2451215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel CAT (Computerized Adaptive Testing) system based on Bayesian network. Our novel system makes good use of topology and probabilistic inference algorithm of Bayesian network to efficiently estimate proficiency of learner and also give an adaptive learning guide when needed. From several experiments, we identified that our system could considerably improve proficiency-estimation performance when compared with conventional CAT methods.\",\"PeriodicalId\":253850,\"journal\":{\"name\":\"IUI '13 Companion\",\"volume\":\"109 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IUI '13 Companion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2451176.2451215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUI '13 Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2451176.2451215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computerized adaptive testing and learning using bayesian network
In this paper, we propose a novel CAT (Computerized Adaptive Testing) system based on Bayesian network. Our novel system makes good use of topology and probabilistic inference algorithm of Bayesian network to efficiently estimate proficiency of learner and also give an adaptive learning guide when needed. From several experiments, we identified that our system could considerably improve proficiency-estimation performance when compared with conventional CAT methods.