This paper is published in Volume-5, Issue-2, 2019
Area
Machine Learning
Author
Sachin B. S.
Co-authors
Shivaprasad K., Somesh T., Sumanth Hegde, Radhika A. D.
Org/Univ
Vidyavardhaka College of Engineering. Mysore, Karnataka, India
Pub. Date
14 March, 2019
Paper ID
V5I2-1259
Publisher
Keywords
word2vec, Deep Neural Networks, doc2vec

Citationsacebook

IEEE
Sachin B. S., Shivaprasad K., Somesh T., Sumanth Hegde, Radhika A. D.. Answer script evaluator: A literature survey, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Sachin B. S., Shivaprasad K., Somesh T., Sumanth Hegde, Radhika A. D. (2019). Answer script evaluator: A literature survey. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2) www.IJARIIT.com.

MLA
Sachin B. S., Shivaprasad K., Somesh T., Sumanth Hegde, Radhika A. D.. "Answer script evaluator: A literature survey." International Journal of Advance Research, Ideas and Innovations in Technology 5.2 (2019). www.IJARIIT.com.

Abstract

Every college, university, school conduct exams and most important part of exams are the results. In order to get these results, the exam papers have to be evaluated one by one manually. This process of evaluating the exam papers is time-consuming and requires more manpower. To overcome this solution, we have come up with a thought that removes the manual evaluation process. Our project focuses on developing a system that evaluates an answer script against a pre-uploaded marking scheme. Initially, the answers are taken in digital format and those digital answers are processed using algorithms such as word2vec where the word’s similarity index is extracted and the words similar to it are noted. Using this we can also get the meaning of the paragraph and we can match it with the answer key and get the match percentage. Using this percentage, we can calculate the marks to be awarded.
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