Skip to main content

Development of Prototype of Natural Language Answer Processor for e-Learning

  • Conference paper
  • First Online:
Artificial Intelligence (RCAI 2020)

Abstract

Automated knowledge control is an important component of e-Learning systems. Moreover, as an analysis of the most recent publications shows, the possibility of automated free natural language answers assessment is almost not represented in modern e-learning systems. Existing educational technologies either support only test approach to knowledge control, or when processing a natural-language answer, its semantic structure is not taken into account sufficiently for accurate assessment. This paper presents a software prototype that implements an algorithm for semantic processing of natural language answers. The basis of the algorithm is a theoretical pragmatically-oriented model proposed by D. Suleymanov, where main methodological principles are the principle of context determinism and the principle of meaning expectation. The implemented prototype was evaluated in order to verify its compliance with theoretical model and obtain the data necessary for further development of model and algorithm.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Suleymanov, D.: Methodology and principles of the intelligent agent design for the textual dialogue systems. In: Proceedings of “System Analysis and Semiotic Modeling” SASM-2011, Fan, Kazan (2011)

    Google Scholar 

  2. Bukharaev, R.G., Suleymanov, D.Sh.: Semanticheskiy analiz v voprosno-otvetnykh sistemakh Kazan University Publishing, Kazan (1990). 123 p.

    Google Scholar 

  3. Burrows, S., Gurevych, I., Stein, B.: The eras and trends of automatic short answer grading. Int. J. Artif. Intell. Educ. 25(1), 60–117 (2014). https://doi.org/10.1007/s40593-014-0026-8

    Article  Google Scholar 

  4. Mishunin, O.B., Savinov, A.P., Firstov, D.I.: State and level of the automatic free-text answer grading systems development. Modern high technologies. Technical Sciences, no. 1, pp. 38–44. Academy of Natural History, Moscow (2016)

    Google Scholar 

  5. Merzlyakov, D.: Generation of regular expressions for automation of written tests checking. Noosphere Society Man, no. 4, pp. 38–44. Academy of Natural History, Moscow (2013)

    Google Scholar 

  6. Attali, Y., Powers, D., Freedman, M., Harrison, M., Obetz, S.: Automated scoring of short-answer open-ended GRE subject test items. GRE Board Research Report, no. GRE-04-02, USA (2008). https://doi.org/10.1002/j.2333-8504.2008.tb02106.x

  7. Dumal, P.A.A., Shanika, W.K.D., Pathinayake, S.A.D., Sandanayake, T.C.: Adaptive and automated online assessment evaluation system. In: 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Malabe, pp. 1–8 (2017). https://doi.org/10.1109/skima.2017.8294135

  8. Srivastava, V., Bhattacharyya, C.: Captivate short answer evaluator. In: 2013 IEEE International Conference in MOOC, Innovation and Technology in Education, Jaipur, pp. 114–119. IEEE (2013). https://doi.org/10.1109/mite.2013.6756317

  9. Pribadi, F.S., Permanasari, A.E., Adji, T.B.: Short answer scoring system using automatic reference answer generation and geometric average normalized-longest common subsequence (GAN-LCS). Educ. Inf. Technol. 23(6), 2855–2866 (2018). https://doi.org/10.1007/s10639-018-9745-z

    Article  Google Scholar 

  10. Kozhevnikov, V.A., Sabinin, O.Yu.: System of automatic verification of answers to open questions in Russian. SPbSPU J. Comput. Sci. Telecommun. Control. Syst. 11(3), 57–72 (2018). https://doi.org/10.18721/JCSTCS.11306

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nikolai Prokopyev .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Suleymanov, D., Prokopyev, N. (2020). Development of Prototype of Natural Language Answer Processor for e-Learning. In: Kuznetsov, S.O., Panov, A.I., Yakovlev, K.S. (eds) Artificial Intelligence. RCAI 2020. Lecture Notes in Computer Science(), vol 12412. Springer, Cham. https://doi.org/10.1007/978-3-030-59535-7_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59535-7_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59534-0

  • Online ISBN: 978-3-030-59535-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics