Integrated Qualitative Response Assessment System

Year : 2024 | Volume : 11 | Issue : 03 | Page : 18 24
    By

    Prasad A. Lahare,

  • Smita K. Thakare,

  1. Assistant Professor, Department of Computer, Pune Vidyarthi Griha’s College of Engineering & S. S. Dhamankar Institute of Management, Nashik, Maharashtra, India
  2. Assistant Professor, Department of Computer, Pune Vidyarthi Griha’s College of Engineering & S. S. Dhamankar Institute of Management, Nashik, Maharashtra, India

Abstract

Examinations for universities and yearboards are traditionally administered offline, with a significant number of students opting for subjective exams. This preference stems from the labor-intensive nature of evaluating subjective responses, which requires considerable time and effort from educators. Additionally, subjective grading can be influenced by the evaluator’s mood, leading to inconsistencies. In contrast, multiple-choice and objective questions are prevalent in entrance and competitive exams due to their ease of automated grading, which is error-free and resource-efficient. Currently, there are no systems available to automate the evaluation of descriptive (subjective) questions, which necessitates manual grading. Automating the evaluation process for descriptive responses could transform the education sector by addressing these challenges. An automated system could utilize technologies such as natural language processing (NLP), machine learning, and optical character recognition (OCR) to evaluate and rate written content. This approach would significantly reduce the time and effort required for grading, ensure consistent and unbiased evaluations, and handle large volumes of exam papers efficiently. Implementing such a system involves several steps: collecting a large dataset of graded papers, developing and training machine learning models, integrating OCR and NLP technologies, and rigorously testing the system for accuracy and reliability. Overcoming challenges such as the complexity and subjectivity of responses, as well as ensuring fairness, is crucial. Ultimately, automated evaluation of descriptive exam responses would enhance the efficiency, consistency, and scalability of the grading process, benefiting both educators and students.

Keywords: Machine learning, natural language processing (NLP), computer-based evaluation, natural language toolkit (NLTK), training phase, support vector machine (SVM)

[This article belongs to Journal of Multimedia Technology & Recent Advancements ]

How to cite this article:
Prasad A. Lahare, Smita K. Thakare. Integrated Qualitative Response Assessment System. Journal of Multimedia Technology & Recent Advancements. 2024; 11(03):18-24.
How to cite this URL:
Prasad A. Lahare, Smita K. Thakare. Integrated Qualitative Response Assessment System. Journal of Multimedia Technology & Recent Advancements. 2024; 11(03):18-24. Available from: https://journals.stmjournals.com/jomtra/article=2024/view=178756


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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 28/06/2024
Accepted 13/09/2024
Published 18/10/2024
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