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  Published Paper Details:

  Paper Title

Machine Learning Based On An Adaptive Approach For Subjective Answer Evaluation.

  Authors

  Mrs. Kavyashree H N,  Mr. Madhur Manohar Dhole,  Mr. Vedant Milind Kakade,  Mr. Prathamesh Suraj Mane

  Keywords

Automated answer verifier, answer verifier, theory answer checker, matching answers.

  Abstract


In the present circumstances, examinations can be classified into two distinct categories: objective and subjective. Competitive exams typically fall into the format of multiple choice questions, which require them to be administered and evaluated on computer screens. At present, most competitive examinations are being held online because of the high volume of students taking part in them. Nevertheless, subjective assessments like board exams are not suitable for computer-based administration. It is essential to incorporate Artificial Intelligence (AI) into our online examination systems. The integration of AI in the management of online exams would significantly streamline the assessment of subjective responses. Moreover, this approach would result in faster and more accurate results. Our proposed system would be carefully designed to replicate the marking process carried out by human evaluators. As a result, this system would be extremely valuable to educational institutions.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTAF02070

  Paper ID - 261047

  Page Number(s) - 351-356

  Pubished in - Volume 12 | Issue 5 | May 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Mrs. Kavyashree H N,  Mr. Madhur Manohar Dhole,  Mr. Vedant Milind Kakade,  Mr. Prathamesh Suraj Mane,   "Machine Learning Based On An Adaptive Approach For Subjective Answer Evaluation.", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.351-356, May 2024, Available at :http://www.ijcrt.org/papers/IJCRTAF02070.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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