Revealing Hidden Impression Topics in Students’ Journals Based on Nonnegative Matrix Factorization

Yuta Taniguchi, D. Suehiro, Atsushi Shimada, H. Ogata
{"title":"Revealing Hidden Impression Topics in Students’ Journals Based on Nonnegative Matrix Factorization","authors":"Yuta Taniguchi, D. Suehiro, Atsushi Shimada, H. Ogata","doi":"10.1109/ICALT.2017.113","DOIUrl":null,"url":null,"abstract":"Students' reflective writings are useful not only for students themselves but also teachers. It is important for teachers to know which concepts were understood well by students and which concepts were not, to continuously improve their classes. However, it is difficult for teachers to thoroughly read the journals of more than one hundred students. In this paper, we propose a novel method to extract common topics and students' common impressions against them from students' journals. Weekly keywords are discovered from journals by scoring noun words with a measure based on TF-IDF term weighting scheme, and then we analyze co-occurrence relationships between extracted keywords and adjectives. We employs nonnegative matrix factorization, one of the topic modeling techniques, to discover the hidden impression topics from the co-occurrence relationships. As a case study, we applied our method on students' journals of the course \"Information Science\" held in our university. Our experimental results show that conceptual keywords are successfully extracted, and four significant impression topics are identified. We conclude that our analysis method can be used to collectively understand the impressions of students from journal texts.","PeriodicalId":134966,"journal":{"name":"2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)","volume":"30 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALT.2017.113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Students' reflective writings are useful not only for students themselves but also teachers. It is important for teachers to know which concepts were understood well by students and which concepts were not, to continuously improve their classes. However, it is difficult for teachers to thoroughly read the journals of more than one hundred students. In this paper, we propose a novel method to extract common topics and students' common impressions against them from students' journals. Weekly keywords are discovered from journals by scoring noun words with a measure based on TF-IDF term weighting scheme, and then we analyze co-occurrence relationships between extracted keywords and adjectives. We employs nonnegative matrix factorization, one of the topic modeling techniques, to discover the hidden impression topics from the co-occurrence relationships. As a case study, we applied our method on students' journals of the course "Information Science" held in our university. Our experimental results show that conceptual keywords are successfully extracted, and four significant impression topics are identified. We conclude that our analysis method can be used to collectively understand the impressions of students from journal texts.
基于非负矩阵分解的学生期刊隐性印象主题揭示
学生的反思性写作不仅对学生自己有用,对老师也有用。对于教师来说,了解学生对哪些概念理解得很好,哪些概念理解得不好,从而不断提高教学质量是很重要的。然而,教师很难通读一百多名学生的期刊。本文提出了一种从学生日记中提取共同话题和学生对共同话题的共同印象的新方法。利用基于TF-IDF词权方案的度量对名词词进行评分,从期刊中发现每周关键词,然后分析提取的关键词与形容词的共现关系。我们采用主题建模技术之一的非负矩阵分解,从共现关系中发现隐藏的印象主题。作为案例研究,我们将该方法应用于我校举办的“情报学”课程的学生期刊。实验结果表明,该方法成功地提取了概念关键词,并识别出了四个重要的印象主题。我们的结论是,我们的分析方法可以用来集体理解学生从期刊文本的印象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:481959085
Book学术官方微信