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Evaluation of Descriptive Exam Answer Scripts using Word Mover’s Distance

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Title Evaluation of Descriptive Exam Answer Scripts using Word Mover’s Distance
 
Creator Murty, M Ramakrisna
Rao, B Tarakeswara
Anuradha, Y
J, Hyma
 
Subject Machine learning
Semantic similarity
Skip gram model
Text mining
 
Description 76-83
The knowledge and competency assessment have paramount significance in the education system. Recent scenario of
COVID-19 witnessed the need of migrating from traditional education system to a modern online learning environment.
Currently in the online assessment process, descriptive exam answer scripts evaluation is one of the tedious tasks to the
teachers. The knowledge assessment may sometimes lead to biasing based on the mood of the evaluator and other
circumstancing parameters. In general, though the evaluation process is well defined, still when two evaluators evaluate the
same scripts, there are very less chances to award the same marks. The proposed model aims to address such real time issues
and outer performs of the evaluation of descriptive answer scripts by using text semantic similarity measure. The proposed
model works based on the word mover’s distance, whose purpose is to measure the semantic similarity among the actual
answer and the answer given by the students. In this work, the data set is generated from the descriptive on-line examination
platform. The data set contains student’s answers, which can pre-process initially and measure the semantic similarity
among key answer and student’s answers. The given automatic evaluation procedure, could guarantee the impartiality and
concealment of the evaluation.
 
Date 2022-01-07T09:39:02Z
2022-01-07T09:39:02Z
2022-01
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58870
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(01) [January 2022]