Automatic Answer Sheet Checker

Authors

  • Pratik Laxman Trimbake  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Swapnali Sampat Kamble  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Rakshanda Bharat Kapoor  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Mr Vishal Kisan Borate  Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India
  • Mr Prashant Laxmanrao Mandale  Assistant Professor, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India

Keywords:

Artificial Intelligence, AI, Software, Database, Keyword Search Algorithm, Stemming Algorithm

Abstract

Nowadays online tests and examinations are becoming popular to reduce the burden of the examination evaluation process. The online exams include either objective or multiple-choice questions. However, subjective-based questions and answers are not involved due to the evaluation process complexity and efficiency of the evaluation process. An automatic answer checker application that checks the written answers and marks the grades similar to a human being will be more helpful for universities and academic institution. The current online exams are conducted and evaluated on machines which can contain only objective questions and there is no provision to extend these into subjective questions. In order to overcome the problems, Artificial Intelligence (AI) based software application is built to check subjective answers by allocating marks to the user automatically, by checking the template answers in the database and the answers written by the user. The proposed system is based on keyword search algorithm that searches keyword provided by admin in the database and stemming algorithm that is used for linguistic normalization to evaluate. As a result of this artificial intelligence-based online answer evaluator, the evaluator's time and energy can be conserved with improved work efficiency.

References

  1. A. Ghanbarpour and H. Naderi, "An Attribute-Specific Ranking Method Based on Language Models for Keyword Search over Graphs," in IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 1, pp. 12-25, 1 Jan. 2020, doi: 10.1109/TKDE.2018.2879863.
  2. A. Ghanbarpour and H. Naderi, "A Model- based Keyword Search Approach for Detecting Top-k Effective Answers," in The Computer Journal, vol. 62, no. 3, pp. 377-393, March 2019, doi: 10.1093/comjnl/bxy056.
  3. C. Roy and C. Chaudhuri, "Case Based Modeling of Answer Points to Expedite Semi-Automated Evaluation of Subjective Papers," 2018 IEEE 8th International Advance Computing Conference (IACC), Greater Noida, India, 2018, pp. 85-90, doi: 10.1109/IADCC.2018.8692133.
  4. S P. Pakray, S. Pal, S. Bandyopadhyay and A. Gelbukh, "Automatic Answer Validation System on English language," 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), Chengdu, China, 2010, pp. V6-329-V6- 333, doi:10.1109/ICACTE.2010.5579166.
  5. V. Hristidis, Y. Papakonstantinou, “Discover: keyword search in rela-tional databases,” Proc. of the 28th international Conference on Very Large Data Bases, VLDB Endowment, Hong Kong, China, pp. 670-681, 2002.
  6. S. Agrawal, S. Chaudhuri, G. Das, “DBXplorer: A System for Keyword- Based Search over Relational Databases,” Proc. of the 18th International Conference on Data Engineering, IEEE Computer Society, pp. 5-16, 2002.
  7. C.-S. Park, S. Lim, “Efficient processing of keyword queries over graph databases for finding effective answers,” Information Processing & Management, vol. 51, no. 1, pp. 42-57, 2015.
  8. J. Coffman, A.C. Weaver, “An Empirical Performance Evaluation of Relational Keyword Search Techniques,” IEEE Tran. on Knowledge and Data Engineering, vol. 26, no. 1, pp. 30-42, 2014.
  9. T. Roelleke, "Information Retrieval Models: Foundations and Relationships," Synthesis Lectures on Information Concepts, Retrieval, and Services, Morgan & Claypool Publishers, pp. 1-163, 2013.
  10. A. Dhokrat, G. Hanumant R, C. Namrata Mahender, “Automated Answering for Subjective Examination”, International Journal of Computer Applications, Volume 56, No.14, pp: 14-17, October 2012.
  11. Sheeba Praveen ”An Approach to Evaluate subjective Questions for Online Examination System” published in International Journal Of Innovative research in Computers and communication Engineering Volume-2, Issue 11, November 2014.
  12. Ani Thomas, MKKowar & Sanjay Sharma “Intelligent Fuzzy Decision Making For Subjective Answer Evaluation using Utility” published by Emerging Trends in Engineering and Technology 2008 ICETET '08 First International conference on Date 16-18 July 2008.
  13. A Gunjal,Mrunal M, Sayli M Pawar and PrakashJ.Kulkarni, “Evaluation of Subjective answers using GLSA enhanced with contextual synonyms”, Published in International Journal on Natural Language processing Computing(INLC) Vol 4.No1,February 2015.

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Published

2021-06-30

Issue

Section

Research Articles

How to Cite

[1]
Pratik Laxman Trimbake, Swapnali Sampat Kamble, Rakshanda Bharat Kapoor, Mr Vishal Kisan Borate, Mr Prashant Laxmanrao Mandale, " Automatic Answer Sheet Checker" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.212-215, May-June-2021.