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Automation and Remote Control

, Volume 73, Issue 6, pp 1029–1045 | Cite as

Generation of integral rating by statistical processing of the test results

  • A. I. Kibzun
  • S. I. Panarin
Intellectual Control Systems

Abstract

The problem of building the rating of a remote training system by processing the results of a run of tests was considered. The Rasch model extended to a run of tests was used. A recurrent algorithm based on the maximum-likelihood procedure and the Newton method was proposed to calculate the rating.

Keywords

Remote Control Newton Method Integral Rating Primary Mark Recurrent Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kibzun, A.I., Karolinskaya, S.N., and Shayukov, R.I., Remote Training System in Mathematics at Institute of Higher Education, Vestn. Komp. Inform. Tekhnol., 2006, no. 4, pp. 29–36.Google Scholar
  2. 2.
    Rasch, G., Probabilistic Models for Some Intelligence and Attainment Tests, Chicago: Univ. Chicago Press, 1980.Google Scholar
  3. 3.
    Andrich, D., Rasch Models for Measurement, in Sage University Paper Series on Quantitative Applications in the Social Sciences, Series no. 07-068, Beverly Hills: Sage Publications, 1988.Google Scholar
  4. 4.
    Bond, T.G. and Fox, Ch.M., Applying the Rasch Model: Fundamental Measurement in the Human Sciences, Mahwah, New Jersey: Lawrence Erlbaum Associates, 2007.Google Scholar
  5. 5.
    Neiman, Yu.M. and Khlebnikov, V.A., Vvedenie v teoriyu modelirovaniya i parametrizatsii pedagogicheskikh testov (Introduction to the Theory of Modeling and Parametrization of Pedagogical Tests), Moscow: Prometei, 2000.Google Scholar
  6. 6.
    Kibzun, A.I., Vishnyakov, B.V., and Panarin, S.I., A Hull of the System of Remote Training in Mathematics, Vestn. Komp. Inform. Tekhnol., 2008, no. 10, pp. 43–48.Google Scholar
  7. 7.
    van der Waerden B.L., Mathematische Statistik, Berlin: Springer, 1957. Translated under the title Matematicheskaya statistika, Moscow: Inostrannaya Literatura, 1960.zbMATHGoogle Scholar
  8. 8.
    Fisher, G., On the Existence and Uniqueness of Maximum-likelihood Estimates in the Rasch Model, Psychometrika, 1981, vol. 46, pp. 59–77.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bertsekas, D., Constrained Optimization and Lagrange Multiplier Methods, New York: Academic, 1982. Translated under the title Uslovnaya optimizatsiya i metody mnozhitelei Lagranzha, Moscow: Radio i Svyaz’, 1987.zbMATHGoogle Scholar
  10. 10.
    Polyak, B.T., Newton Method and Its Role in Optimization and Computational Mathematics, Tr. Int. Sist. Anal., 2006, no. 28, pp. 44–62.Google Scholar
  11. 11.
    Polyak, B.T., Vvedenie v optimizatsiyu, Moscow: Nauka, 1983. Translated into English under the title Introduction to Optimization, New York: Optimization Software, 1987.zbMATHGoogle Scholar
  12. 12.
    Kryanev, A.V. and Lukin, G.V., Metody obrabotki neopredelennykh dannykh (Methods to Process Uncertain Data), Moscow: Fizmatlit, 2006.zbMATHGoogle Scholar
  13. 13.
    Kolmogorov, A.N. and Fomin, S.V., Elementy teorii funktsii i funktsional’nogo analiza (Elements of the Function Theory and the Functional Analysis), Moscow: Fizmatlit, 2004.Google Scholar
  14. 14.
    Wright, B.D. and Masters, G.N., Rating Scales Analysis, Chicago: MESA, 1982.Google Scholar
  15. 15.
    Smith, R.M., Schumacker, R.E., and Bush, M.J., Using Item Mean Squares to Evaluate Fit to the Rasch Model, J. Outcome Meas., 1998, vol. 2, pp. 66–78.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2012

Authors and Affiliations

  • A. I. Kibzun
    • 1
  • S. I. Panarin
    • 1
  1. 1.Moscow State Aviation InstituteMoscowRussia

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