解释矿业

Bhavya, Chengxiang Zhai
{"title":"解释矿业","authors":"Bhavya, Chengxiang Zhai","doi":"10.1145/3386527.3406738","DOIUrl":null,"url":null,"abstract":"Explanations are used to provide an understanding of a concept, procedure, or reasoning to others. Although explanations are present online ubiquitously within textbooks, discussion forums, and many more, there is no way to mine them automatically to assist learners in seeking an explanation. To address this problem, we propose the task of Explanation Mining. To mine explanations of educational concepts, we propose a baseline approach based on the Language Modeling approach of information retrieval. Preliminary results suggest that incorporating knowledge from a model trained on the ELI5 (Explain Like I'm Five) dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result, it can be deployed for multiple online courses. We also suggest some interesting future work in the computational analysis of explanations.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Explanation Mining\",\"authors\":\"Bhavya, Chengxiang Zhai\",\"doi\":\"10.1145/3386527.3406738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Explanations are used to provide an understanding of a concept, procedure, or reasoning to others. Although explanations are present online ubiquitously within textbooks, discussion forums, and many more, there is no way to mine them automatically to assist learners in seeking an explanation. To address this problem, we propose the task of Explanation Mining. To mine explanations of educational concepts, we propose a baseline approach based on the Language Modeling approach of information retrieval. Preliminary results suggest that incorporating knowledge from a model trained on the ELI5 (Explain Like I'm Five) dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result, it can be deployed for multiple online courses. We also suggest some interesting future work in the computational analysis of explanations.\",\"PeriodicalId\":20608,\"journal\":{\"name\":\"Proceedings of the Seventh ACM Conference on Learning @ Scale\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Seventh ACM Conference on Learning @ Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3386527.3406738\",\"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 Seventh ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3386527.3406738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

解释用于向他人提供对概念、程序或推理的理解。尽管在线教科书、讨论论坛和其他很多地方都有解释,但没有办法自动挖掘它们来帮助学习者寻找解释。为了解决这个问题,我们提出了解释挖掘的任务。为了挖掘教育概念的解释,我们提出了一种基于信息检索的语言建模方法的基线方法。初步结果表明,将ELI5 (Explain Like I’m Five)数据集上训练的模型中的知识以文档的形式合并在一起,有助于提高标准检索模型的性能。这是令人鼓舞的,因为我们的方法需要最少的域内监督,因此,它可以部署到多个在线课程中。我们还建议在解释的计算分析方面进行一些有趣的未来工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Explanation Mining
Explanations are used to provide an understanding of a concept, procedure, or reasoning to others. Although explanations are present online ubiquitously within textbooks, discussion forums, and many more, there is no way to mine them automatically to assist learners in seeking an explanation. To address this problem, we propose the task of Explanation Mining. To mine explanations of educational concepts, we propose a baseline approach based on the Language Modeling approach of information retrieval. Preliminary results suggest that incorporating knowledge from a model trained on the ELI5 (Explain Like I'm Five) dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result, it can be deployed for multiple online courses. We also suggest some interesting future work in the computational analysis of explanations.
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
期刊最新文献
Trust, Sustainability and [email protected] L@S'22: Ninth ACM Conference on Learning @ Scale, New York City, NY, USA, June 1 - 3, 2022 L@S'21: Eighth ACM Conference on Learning @ Scale, Virtual Event, Germany, June 22-25, 2021 Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1