将学生反应时间和导师教学干预纳入学生建模

Chen Lin, Shitian Shen, Min Chi
{"title":"将学生反应时间和导师教学干预纳入学生建模","authors":"Chen Lin, Shitian Shen, Min Chi","doi":"10.1145/2930238.2930291","DOIUrl":null,"url":null,"abstract":"Bayesian Knowledge Tracing (BKT) is one of the most widely adopted student-modeling methods. It uses performance (incorrect,correct) to infer student knowledge state (unlearned, learned). However, performance can be noisy and thus we explored another type of observations -- student response time. Furthermore, we proposed Intervention Bayesian Knowledge Tracing (Intervention-BKT) which can incorporate multiple types of instructional interventions into the conventional BKT model. Our results show that for next-step performance predictions, Intervention-BKT is more effective than BKT; whereas to predict students' post-test scores, including student response time would yield better result than using performance alone.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Incorporating Student Response Time and Tutor Instructional Interventions into Student Modeling\",\"authors\":\"Chen Lin, Shitian Shen, Min Chi\",\"doi\":\"10.1145/2930238.2930291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Bayesian Knowledge Tracing (BKT) is one of the most widely adopted student-modeling methods. It uses performance (incorrect,correct) to infer student knowledge state (unlearned, learned). However, performance can be noisy and thus we explored another type of observations -- student response time. Furthermore, we proposed Intervention Bayesian Knowledge Tracing (Intervention-BKT) which can incorporate multiple types of instructional interventions into the conventional BKT model. Our results show that for next-step performance predictions, Intervention-BKT is more effective than BKT; whereas to predict students' post-test scores, including student response time would yield better result than using performance alone.\",\"PeriodicalId\":339100,\"journal\":{\"name\":\"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2930238.2930291\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2930238.2930291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21

摘要

贝叶斯知识追踪(BKT)是目前应用最广泛的学生建模方法之一。它用表现(不正确、正确)来推断学生的知识状态(未学习、已学习)。然而,表现可能是嘈杂的,因此我们探索了另一种类型的观察——学生的反应时间。此外,我们提出了干预贝叶斯知识追踪(Intervention-BKT),它可以将多种类型的教学干预纳入传统的BKT模型中。结果表明,对于下一步的绩效预测,干预-BKT比BKT更有效;而在预测学生的测试后分数时,包括学生的反应时间比单独使用表现会产生更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incorporating Student Response Time and Tutor Instructional Interventions into Student Modeling
Bayesian Knowledge Tracing (BKT) is one of the most widely adopted student-modeling methods. It uses performance (incorrect,correct) to infer student knowledge state (unlearned, learned). However, performance can be noisy and thus we explored another type of observations -- student response time. Furthermore, we proposed Intervention Bayesian Knowledge Tracing (Intervention-BKT) which can incorporate multiple types of instructional interventions into the conventional BKT model. Our results show that for next-step performance predictions, Intervention-BKT is more effective than BKT; whereas to predict students' post-test scores, including student response time would yield better result than using performance alone.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信