A New Roadmap for Evaluating Descriptive Handwritten Answer Script

Shivakumara, P; Pal, U; Lu, T; Chakraborti, T; Blumenstein, M

A New Roadmap for Evaluating Descriptive Handwritten Answer Script

Shivakumara, P Pal, U Lu, T Chakraborti, T Blumenstein, M

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Publisher:: World Scientific
Publication Type:: Chapter
Citation:: Frontiers in Pattern Recognition and Artificial Intelligence, 2019, pp. 83-96
Issue Date:: 2019-06-12

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Field	Value	Language
dc.contributor.author	Shivakumara, P
dc.contributor.author	Pal, U
dc.contributor.author	Lu, T
dc.contributor.author	Chakraborti, T
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.date.accessioned	2023-01-03T22:08:58Z
dc.date.available	2023-01-03T22:08:58Z
dc.date.issued	2019-06-12
dc.identifier.citation	Frontiers in Pattern Recognition and Artificial Intelligence, 2019, pp. 83-96
dc.identifier.isbn	978-981-120-352-7
dc.identifier.uri	http://hdl.handle.net/10453/164651
dc.description.abstract	In computational pedagogy, a relatively simple Optical Character Recognizer system can robustly evaluate objective response types automatically without human intervention. This saves time, cost and man-hours. Thus, the next question becomes whether it is possible to develop an automated system for evaluating descriptive handwritten answer types. Recent experiences show that human evaluation of long examination responses is quite subjective and prone to challenges like inexperience, negligence, lack of uniformity in the case of several evaluators, etc. In this work, we present the roadmap for an automated vision system that evaluates descriptive answers based on extracting relevant words and finding the relationship between words according to weights. We introduce context features to handle variations of words written by different users to estimate the final score.
dc.format.extent	14
dc.language	en
dc.publisher	World Scientific
dc.relation.ispartof	Frontiers in Pattern Recognition and Artificial Intelligence
dc.relation.isbasedon	10.1142/9789811203527_0005
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A New Roadmap for Evaluating Descriptive Handwritten Answer Script
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-01-03T22:08:56Z
pubs.place-of-publication	USA
pubs.publication-status	Published
dc.location	USA

Abstract:

In computational pedagogy, a relatively simple Optical Character Recognizer system can robustly evaluate objective response types automatically without human intervention. This saves time, cost and man-hours. Thus, the next question becomes whether it is possible to develop an automated system for evaluating descriptive handwritten answer types. Recent experiences show that human evaluation of long examination responses is quite subjective and prone to challenges like inexperience, negligence, lack of uniformity in the case of several evaluators, etc. In this work, we present the roadmap for an automated vision system that evaluates descriptive answers based on extracting relevant words and finding the relationship between words according to weights. We introduce context features to handle variations of words written by different users to estimate the final score.

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http://hdl.handle.net/10453/164651