Skip to main content

EVaClassifier Using Linear SVM Machine Learning Algorithm

  • Conference paper
  • First Online:
Intelligent Computing and Communication (ICICC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1034))

Included in the following conference series:

  • 598 Accesses

Abstract

Evaluating descriptive answer scripts is one of the challenging tasks for academicians along with their routine works and increase in the number of students enrolling in educational institution. It involves various factors such as man power, time, cost, and mental health. These factors are directly proportional to students’ strengths. Hence, evaluation scheme needs to be automated to ease the work of staff. Many research activities have been carried out to automate the evaluation process and easier the work of staff. In this paper, an attempt is made to propose two classes Eva classifier using Support Vector Machine Supervised Machine Learning algorithm for auto evaluating short answers and performance of the classifier is evaluated using accuracy of answer classification.

Please note that the LNCS Editorial assumes that all authors have used the western naming convention, with given names preceding surnames. This determines the structure of the names in the running heads and the author index.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Protest Over Delay in Evaluation Work. http://www.thehindu.com/news/cities/

  2. 80 out of 83 Score More After Revaluation. http://articles.timesofindia.indiatimes.com

  3. Revaluation Fails 100 ‘Passed’ PU Students. http://www.bangaloremirror.com/index.aspx

  4. Siddhartha, G.: e-Examiner: A System for Online Evaluation and Grading of Essay Questions. http://elearn.cdac.in/eSikshak/eleltechIndia05/PDF

  5. Ramamurthy, M., Krishnamurthi, I., Ilango, S., Palaniappan, S.: Discrete Model Based Answer Script Evaluation Using Decision Tree Rule Classifier, pp. 1–12 (2019)

    Google Scholar 

  6. Alrehily, A.D., Siddiqui, M.A., Buhari, S.M.: Intelligent electronic assessment for subjective exam, ACSIT, ICITE, SIPM, pp. 47–63 (2018)

    Google Scholar 

  7. Kirithika, R., Jayashree, N.: Learning to Grade Short Answers using Machine Learning Techniques, WCI ’15, August 10–13, 2015

    Google Scholar 

  8. Basu, S., Jacobs, C. and Vanderwende, L.: Powergrading: a clustering approach to amplify human effort for short answer grading. Trans. Associat. Computat. Linguistics (2013)

    Google Scholar 

  9. Nlp.stanford.edu. The stanford nlp (natural language processing) group (2015)

    Google Scholar 

  10. Mohler, M., Bunescu, R. and Mihalcea, R.: Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments. Association for Computational Linguistics (2011)

    Google Scholar 

  11. Mohler, M., Mihalcea, R.: Text-to-text Semantic Similarity for Automatic Short Answer Grading. Association of Computational Linguistics (2009)

    Google Scholar 

  12. Shermis, M.D., Hamner, B.: Contrasting State-of-the-Art Automated Scoring of Essays: Analysis. Contrasting Essay Scoring, pp. 1–54 (2012)

    Google Scholar 

  13. Latifi, S.M.F., Guo, Q., Gierl, M.J., Mousavi, A., Fung, K., Lacroix, D.: Towards Automated Scoring using Open-Source Technologies. Annual Meeting of the Canadian Society for the Study of Education, pp. 13–14 (2013)

    Google Scholar 

  14. Kumar, S. and Sree, R.R.: Experiments towards determining best training sample size for automated evaluation of descriptive answers through sequential minimal optimization. ICTACT J. Soft Comput. 4(2), 710 –714 (2014)

    Google Scholar 

  15. Text Categorization with Support Vector Machines. http://www.cs.cornell.edu/people/tj/publications/joachims_98a.pdf

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. Vinothina .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vinothina, V., Prathap, G. (2020). EVaClassifier Using Linear SVM Machine Learning Algorithm. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_48

Download citation

Publish with us

Policies and ethics