Subjective Answer Grader System Based on Machine Learning

  • Avani SakhaparaEmail author
  • Dipti Pawade
  • Bhakti Chaudhari
  • Rishabh Gada
  • Aakash Mishra
  • Shweta Bhanushali
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


According to experts, a good test paper should have a combination of objective and subjective questions. But the current online examinations mainly consist of only objective questions. This is because an accurate computerized grading is possible for such questions. But achieving an accurate computerized grading for the subjective questions is still a matter of concern. To address this problem, in this paper, we have designed and implemented a machine learning-based subjective answer grader system (SAGS) using two algorithms, namely latent semantic analysis (LSA) and information gain (IG) for the generation of grades. We have proposed the enhancement of these algorithms through synonym replacement using WordNet, and the accuracy of these algorithms is measured by comparing the generated scores with the scores given by human evaluators.


Subjective answer evaluation Machine learning Latent semantic analysis (LSA) Information gain (IG) WordNet 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Avani Sakhapara
    • 1
    Email author
  • Dipti Pawade
    • 1
  • Bhakti Chaudhari
    • 1
  • Rishabh Gada
    • 1
  • Aakash Mishra
    • 1
  • Shweta Bhanushali
    • 1
  1. 1.Department of ITK.J. Somaiya College of EngineeringMumbaiIndia

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