vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffadc416000000002f030001000700 Automation of descriptive answers evaluation is the need of the hour because of the huge increase in the number of students enrolling each year in educational institutions and the limited staff available to spare their time for evaluations. In this paper, we use a machine learning workbench called LightSIDE to accomplish auto evaluation and scoring of descriptive answers. We attempted to identify the best supervised machine learning algorithm given a limited training set sample size scenario. We evaluated performances of Bayes, SVM, Logistic Regression, Random forests, Decision stump and Decision trees algorithms. We confirmed SVM as best performing algorithm based on quantitative measurements across accuracy, kappa, training speed and prediction accuracy with supplied test set.
C. Sunil Kumar1, R. J. Rama Sree2
Bharathiar University, Coimbatore, India1, Rashtriya Sanskrit Vidyapeetha, India2
Descriptive Answers, Automated Evaluation, LightSIDE, Machine Learning Algorithms
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 4 , Issue: 4 , Pages: 781-786 )
Date of Publication :
July 2014
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