Use of Syntactic Similarity Based Similarity Matrix for Evaluating Descriptive Answer

D. V. Paul, J. Pawar
{"title":"Use of Syntactic Similarity Based Similarity Matrix for Evaluating Descriptive Answer","authors":"D. V. Paul, J. Pawar","doi":"10.1109/T4E.2014.60","DOIUrl":null,"url":null,"abstract":"Reforms in the educational system emphasize more on continuous assessment. The descriptive examination question paper when compared to objective question paper acts as a better aid in continuous assessment for testing the progress of a student under various cognitive levels at different stages of learning. Unfortunately, assessment of descriptive answers is found to be tedious and time consuming by instructors due to the increase in number of examinations in continuous assessment system. In this paper, an attempt has been made to address the problem of automatic evaluation of descriptive answer using vector based similarity matrix with order based word-to-word syntactic similarity measure. Word order similarity measure remains as one of the best measure to find the similarity between sequential words in sentences and is increasing its popularity due to its simple interpretation and easy computation. To the best of our knowledge no work has been carried out for automatic evaluation of descriptive answer using word order vectors. The experimental results prove that this approach is promising for application in automatic evaluation of descriptive answer paper.","PeriodicalId":151911,"journal":{"name":"2014 IEEE Sixth International Conference on Technology for Education","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Sixth International Conference on Technology for Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/T4E.2014.60","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

Reforms in the educational system emphasize more on continuous assessment. The descriptive examination question paper when compared to objective question paper acts as a better aid in continuous assessment for testing the progress of a student under various cognitive levels at different stages of learning. Unfortunately, assessment of descriptive answers is found to be tedious and time consuming by instructors due to the increase in number of examinations in continuous assessment system. In this paper, an attempt has been made to address the problem of automatic evaluation of descriptive answer using vector based similarity matrix with order based word-to-word syntactic similarity measure. Word order similarity measure remains as one of the best measure to find the similarity between sequential words in sentences and is increasing its popularity due to its simple interpretation and easy computation. To the best of our knowledge no work has been carried out for automatic evaluation of descriptive answer using word order vectors. The experimental results prove that this approach is promising for application in automatic evaluation of descriptive answer paper.
基于句法相似性的相似性矩阵在描述性答案评价中的应用
教育体制改革更加强调持续评估。描述性考题与客观考题相比,可以更好地辅助学生在不同学习阶段、不同认知水平下的学习进度。不幸的是,由于连续评估系统中考试次数的增加,教师发现描述性答案的评估繁琐且耗时。本文尝试用基于向量的相似矩阵和基于顺序的词对词句法相似度量来解决描述性答案的自动评价问题。词序相似度度量由于其简单的解释和易于计算的优点,一直是寻找句子中顺序词之间相似度的最佳度量之一。据我们所知,还没有使用词序向量对描述性答案进行自动评估的工作。实验结果表明,该方法在描述性答卷自动评价中具有较好的应用前景。
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