Difference between revisions of "Web Authoriser Tool to Build Assessments Using Wikipedia Articles"

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'''Web Authoriser Tool to Build Assessments Using Wikipedia Articles''' - scientific work related to Wikipedia quality published in 2017, written by S. S. R. Adithya and Pramod Kumar Singh.
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'''Web Authoriser Tool to Build Assessments Using Wikipedia Articles''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[S. S. R. Adithya]] and [[Pramod Kumar Singh]].
  
 
== Overview ==
 
== Overview ==
Improving and assessing knowledge on a topic is need of the current generation. In the traditional system, experts create assessments manually by reading articles and generating questions on a topic. However, it is very effort and time consuming to read and create questions for every article. Therefore, learners find it difficult to assess their knowledge on a topic owing to no- or low-availability of assessments. Usually, they rely on books or question banks. With fast updating technology and availability of good web resources, the way assessments are created may be improved. This paper introduces a system for self-assessment using Wikipedia article as a source of data for the assessment. The system generates a set of questions with appropriate choices by searching for an article from the Wikipedia. This work primarily focuses on identifying locations and numbers to generate questions using regular expressions and parts of speech (POS) tagging. The quality of generated questions, in terms of precision, differs from article to article. The precision is very high in case of popular articles whereas, for not so popular articles it is moderate.
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Improving and assessing knowledge on a topic is need of the current generation. In the traditional system, experts create assessments manually by reading articles and generating questions on a topic. However, it is very effort and time consuming to read and create questions for every article. Therefore, learners find it difficult to assess their knowledge on a topic owing to no- or low-availability of assessments. Usually, they rely on books or question banks. With fast updating technology and availability of good web resources, the way assessments are created may be improved. This paper introduces a system for self-assessment using [[Wikipedia]] article as a source of data for the assessment. The system generates a set of questions with appropriate choices by searching for an article from the Wikipedia. This work primarily focuses on identifying locations and numbers to generate questions using regular expressions and parts of speech (POS) tagging. The quality of generated questions, in terms of precision, differs from article to article. The precision is very high in case of popular articles whereas, for not so popular articles it is moderate.

Latest revision as of 19:49, 14 June 2019

Web Authoriser Tool to Build Assessments Using Wikipedia Articles - scientific work related to Wikipedia quality published in 2017, written by S. S. R. Adithya and Pramod Kumar Singh.

Overview

Improving and assessing knowledge on a topic is need of the current generation. In the traditional system, experts create assessments manually by reading articles and generating questions on a topic. However, it is very effort and time consuming to read and create questions for every article. Therefore, learners find it difficult to assess their knowledge on a topic owing to no- or low-availability of assessments. Usually, they rely on books or question banks. With fast updating technology and availability of good web resources, the way assessments are created may be improved. This paper introduces a system for self-assessment using Wikipedia article as a source of data for the assessment. The system generates a set of questions with appropriate choices by searching for an article from the Wikipedia. This work primarily focuses on identifying locations and numbers to generate questions using regular expressions and parts of speech (POS) tagging. The quality of generated questions, in terms of precision, differs from article to article. The precision is very high in case of popular articles whereas, for not so popular articles it is moderate.