Skip to main content
SpringerLink
Log in

Using syntactic text analysis to estimate educational tasks’ difficulty and complexity

  • Large Scale Systems Control
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

We suggest a routine for automatic estimation of complexity and difficulty of educational tasks. This routine is based on syntactic text analysis, phrases’ predicative structures identification, and semantic network construction. Then we develop a mathematical model which employs a notion on semantic distance between notions–words to calculate the amount of knowledge in a semantic network. We show that the amount of knowledge in a semantic network is a measure in the set of all semantic networks, and the semantic distance makes this set the metric one.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Avanesov, B.S., Knowledge as a Studies Course of Pedagogical Measuremnts, Pedagogich. Izmeren., 2005, no. 3, pp. 43–52.

    Google Scholar 

  2. Anisimov, A.V., Liman, K.S., and Marchenko, A.A., Methods of Computing Measures of Words Semantic Similarity in a Natural Language, URL: http://lingvoworks.org.ua (Cited April 9, 2013).

    Google Scholar 

  3. Borisov, I.K., Optimizatsiya protsessa obucheniya: Obshchedidakticheskii aspekt (Optimization of Education Process. Common Pedagogical Aspect), Moscow: Pedagogika, 1977.

    Google Scholar 

  4. Ball, G.A., Teoriya uchebniykh zadach: psikhologo-pedagogicheskii aspekt (Educational Tasks Theory: Psychological-and-Pedagogical Aspect), Moscow: Pedagogika, 1990.

    Google Scholar 

  5. Belonogov, G.G., Komp’yuternaya lingvistika i perspektivnye informatsionnye tekhnologii (Computational Linguistics and Advanced Information Technology), Moscow: Russkii Mir, 2004.

    Google Scholar 

  6. Braslavski, P.I. and Sokolov, E.A., Comparison of Five Methods for Variable Length Term Extraction, Tr. mezhd. konf. “Dialog 2008” (Proc. Int. Conf. “Dialog 2008”), 2008, pp. 67–74.

    Google Scholar 

  7. Vagin, V.N., Golovina, E.Yu., Zagoryanskaya, A.A., and Fomina, M.V., Dostovernyi i pravdopodobnyi vyvod v intellektual’nykh sistemakh (Exact and Plausible Inference in Intelligent Systems), Moscow: Fizmatlit, 2008.

    Google Scholar 

  8. Valgina, N.S., Rosenthal’, D.E., and Fomina, M.I., Sovremennyi russkii yazyk: uchebnik (Modern Russian Language: Textbook), Valgina, N.S., Ed., Moscow: Logos, 2002.

  9. Gavrilova, T.A. and Khoroshevsky, V.F., Bazy znanii intellektual’nykh sistem (Knowledge Bases of Intelligent Systems), St.-Petersburg: Piter, 2000.

    Google Scholar 

  10. Danil’yan, O.G. and Panova, N.I., Sovremennyi slovar’ po obshchestvennym naukam (The Modern Dictionary on Social Studies), Moscow: Eksmo-Press, 2007.

    Google Scholar 

  11. Evstigneev, V.A., Primenenie teorii grafov v programmirovanii (Application of Graph Theory to Programming), Moscow: Nauka, 1985.

    MATH  Google Scholar 

  12. Ermakov, A.E., Knowledge Extraction from Text and Its Processing: Current State and Prospects, Inform. Tekhn., 2009, no. 7, pp. 50–55.

    Google Scholar 

  13. Efremova, N.F., Testovyi kontrol’ v obrazovanii: uchebnoe posobie (Test Control in Education: Textbook), Moscow: Universitetskaya Kniga, 2007.

    Google Scholar 

  14. Karpenko, A.P. and Sokolov, N.K., The Semantic Net Complexity Measures in Learning System, Vestn. MGTU, Ser. Priborostr., 2009, no. 1(74), pp. 50–66.

    Google Scholar 

  15. Karpenko, M.P., The Problem of Measurement of Knowledge and Education Technologies, Zh. Prakt. Psikhol., 1997, no. 4, pp. 74–79.

    Google Scholar 

  16. Kravtsov, L.G., Methodological Problems in Psychological Analysis of Thought in Notions, Mater. I Ros. konf. kogn. nauke (Mat. 1st Rus. Conf. Cogn. Sci.), Kazan: Kazan. Gos. Univ., 2004, URL: http://www.ksu.ru/ss/cogsci04/science/cogsci04/sod.php3 (Cited November 22, 2013).

    Google Scholar 

  17. Krasnykh, V.V., Osnovy psikholingvistiki i teorii kommunikatsii (Fundamentals of Psycholinguistics and Theory of Communication), Moscow: Gnosis, 2012.

    Google Scholar 

  18. Lerner, I.Ya., Complexity Factors in Cognitive Problems, Nov. Issled. Pedagog. Nauk., 1970, no. 1, pp. 86–91.

    Google Scholar 

  19. Luger, G.F., Artificial Intelligence: Structures and Strategies for Complex Problem Solving, London: Addison-Wesley, 2002. Translated under the title Iskusstvennyi intellekt: strategii i metody resheniya slozhnykh problem, Moscow: Vil’yams, 2005.

    Google Scholar 

  20. Mikk, Ya.A., Optimizatsiya slozhnosti uchebnogo teksta: v pomoshch’ avtoram i redaktoram (Optimization of Complexity of the Educational Text: For the Aid to Authors and Editors), Moscow: Prosveshchenie, 1981.

    Google Scholar 

  21. Mitrofanova, O.A., Semantic Distances: Problems and Prospects, XXXIV Mezhd. filolog. konf: Vypusk 21. “Prikladnaya i matematicheskaya lingvistika” (34 Int. Phil. Conf.: Issue 21. “Applied and Mathematical Linguistics”), St.-Petersburg, 2005, pp. 59–63.

    Google Scholar 

  22. Multifunctional Linguistic Processor ETAP-3, URL: http://www.iitp.ru/ru/science.

  23. Naikhanova, L.V., Tekhnologiya sozdaniya metodov avtomaticheskogo postroeniya ontologii s primeneniem geneticheskogo i avtomatnogo programmirovaniya (Technology of Creating Methods for Automatic Construction of Ontology Using Genetic and Automata-Based Programming), Ulan-Ude: BNTs,, 2008.

    Google Scholar 

  24. Novaya filosofskaya entsiklopediya (New Encyclopaedia of Philosophy), 4 vols., Moscow: Mysl’, 2010, URL: http://iph.ras.ru/enc.htm (Cited April 9, 2014).

  25. Noveishii filosofskii slovar’ (Newest Philosophical Dictionary), Minsk: Knizhnyi Dom, 2003, 3rd ed.

  26. Pogorelov, A.V., Geometriya: uchebnik dlya 7–11 klassov obshcheobrazovatel’nykh uchrezhdenii (Geometry: Textbook for Secondary School), Moscow: Prosveshchenie, 1995.

    Google Scholar 

  27. Popov, E.V., Obshchenie c EVM na estestvennom yazyke (Natural Language Interaction with Computers), Moscow: Nauka, 1982.

    Google Scholar 

  28. Romadina, O.G. and Rakitina, E.A., Procedure of the Estimation of Complexity of Educational Problems on Computer Science, Vopr. Sovrem. Nauki Prakt., 2010, no. 10–12(31), pp. 146–151.

    Google Scholar 

  29. Ryzhenko, N.G., Graphic Modeling as a Method for Defining Complexity of Solutions to School Text Problems, Matem. Inform., Omsk, 2001, issue 1, pp. 99–103.

    Google Scholar 

  30. Fedotov, I.E., Nekotorye priemy parallel’nogo programmirovaniya: uchebnoe posobie (Some Methods of Parallel Programming: Textbook), Moscow: MGIREA, 2008.

    Google Scholar 

  31. Filippovich, Yu.N. and Prokhorov, A.V., Semantika informatsionnykh tekhnologii: opyty slovarnotezaurusnogo opisaniya (Semantics of Information Technologies: Experience of Dictionary-Thesaurus Description), Moscow: MGUP, 2002.

    Google Scholar 

  32. Collins-Tompson, K., Bennett, P., White, R., Chica, S., and Sontag, D., Personalizing Web Search Results by Reading Level, Proc. 20th ACM Int. Conf. on Information and Knowledge Management, New York, 2011, pp. 403–412.

    Google Scholar 

  33. Dubay, W., The Principles of Readability, Costa Mesa: Impact Information, 2004.

    Google Scholar 

  34. Hartley, R.V.L., Transmission of Information, Bell Syst. Tech. J., 1928, July, pp. 535–563.

    Google Scholar 

  35. Leacock, C. and Chodorow, M., Combining Local Context and WordNet Similarity for Word Sense Identification, in WordNet: An Electronic Lexical Database, Fellbaum, C., Ed., Boston: MIT Press, 1998, pp. 265–283.

    Google Scholar 

  36. Patwardhan, S., Banerjee, S., and Pedersen, T., UsingMeasures of Semantic Relatedness forWord Sense Disambiguation, Proc. 4th Int. Conf. on Intelligent Text Processing and Computational Linguistics, 2003, pp. 241–257.

    Chapter  Google Scholar 

  37. Shannon, C.E., A Mathematical Theory of Communication, Bell Sys. Tech. J., 1948, vol. 27, pp. 379–423, 623–656.

    Article  MathSciNet  MATH  Google Scholar 

  38. Anisimovich, K.V., Druzhkin, K.Ju., Minlos, F.R., Petrova, M.A., Selegey, V.P., and Zuev, K.A., Syntactic and Semantic Parser Based on ABBYY Compreno Linguistic Technologies, in Kom’yuternaya lingvistika i intellektual’nye tekhnologii (Computer Linguistics and Intellectual Technologies), 2012, pp. 810–822, URL: http://www.dialog-21.ru/digests/dialog2012/materials/pdf/Anisimovich.pdf (Cited December 2, 2013).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

    I. S. Naumov & V. S. Vykhovanets

Authors
  1. I. S. Naumov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. S. Vykhovanets
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. S. Naumov.

Additional information

Original Russian Text © I.S. Naumov, V.S. Vykhovanets, 2014, published in Upravlenie Bol’shimi Sistemami, 2014, No. 48, pp. 97–131.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naumov, I.S., Vykhovanets, V.S. Using syntactic text analysis to estimate educational tasks’ difficulty and complexity. Autom Remote Control 77, 159–178 (2016). https://doi.org/10.1134/S0005117916010100

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117916010100

Keywords

Search

Navigation