Automated Grading System for Subjective Answers Evaluation using Machine Learning and NLP

Authors: Abhay Rayate, Sonali Ghumare, Fazila Sayyed, Harshal Sapkal, Sharad Rokade

Country: India

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Abstract: Every year boards and universities exams are conducted offline mode. Large number of students attend subjective type exams. For evaluation of such large number of papers manually required hard efforts. Sometimes quality of evaluation may change according to mood of evaluator. The evaluation work is very lengthy and time consuming. Competitive and entrance exams typically contain objective or multiple choice questions. These exams are evaluated on machine as they conducted on machine and therefore their evaluation is easy. It also saves multiple resources and human interaction and hence it is errorless. There are multiple systems available for evaluation objective (MCQ) type question but there is no provision for subjective (Descriptive) type question. It will be very helpful for educational institutions if the process of evaluation of descriptive answers is automated to capably assess student’s exam answer sheets.

Keywords: Subjective Answer Evaluation, Big Data, Machine Learning, Natural Language Processing, Word2vec.

Paper Id: 230566

Published On: 2024-04-14

Published In: Volume 12, Issue 2, March-April 2024

Cite This: Automated Grading System for Subjective Answers Evaluation using Machine Learning and NLP - Abhay Rayate, Sonali Ghumare, Fazila Sayyed, Harshal Sapkal, Sharad Rokade - IJIRMPS Volume 12, Issue 2, March-April 2024.

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