Probabilistic method of filtering artifacts in adaptive testing

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Abstract

In this article we present a method of filtering the results of adaptive testing, built on the use of structures in the form of Markov models with continuous time. The elimination of artifacts caused by various forms of incorrect direct intervention in the procedure of testing is performed on the basis of comparison of the observed and the predicted results of the answers to the questions using the Kalman filter, adapted for the solution of the considered problem.

General Information

Keywords: adaptive testing, Markov models, Kalman filter

Journal rubric: Mathematical Psychology

Article type: scientific article

For citation: Kuravsky L.S., Yuryev G.A. Probabilistic method of filtering artifacts in adaptive testing. Eksperimental'naâ psihologiâ = Experimental Psychology (Russia), 2012. Vol. 5, no. 1, pp. 119–131. (In Russ., аbstr. in Engl.)

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References

  1. Avanesov V. S. Pedagogicheskoe izmerenie latentnyh kachestv // Pedagogicheskaja diagnostika. 2003. № 4. S. 69–78.
  2. Kardanova E. Ju. Modelirovanie i parametrizacija testov: osnovy teorii i prilozhenija. M.: FGU «Federal'nyj centr testirovanija», 2008.
  3. Kramer G. Matematicheskie metody statistiki. M.: Mir, 1976.
  4. Kuravskij L. S., Baranov S. N. Sintez setej Markova dlja prognozirovanija ustalostnogo razrushenija // Nejrokomp'jutery: razrabotka i primenenie. 2002. № 11. S. 29–40.
  5. Kuravskij L. S., Baranov S. N. Primenenie nejronnyh setej dlja diagnostiki i prognozirovanija ustalostnogo razrushenija tonkostennyh konstrukcij // Nejrokomp'jutery: razrabotka i primenenie. 2001. № 12. S. 47–63.
  6. Kuravskij L. S., Baranov S. N., Kornienko P. A. Obuchaemye mnogofaktornye seti Markova i ih primenenie dlja issledovanija psihologicheskih harakteristik // Nejrokomp'jutery: razrabotka i primenenie. 2005. № 12. S. 65–76.
  7. Kuravskij L. S., Baranov S. N., Malyh S. B. Nejronnye seti v zadachah prognozirovanija, diagnostiki i analiza dannyh: Ucheb. posobie. M.: RUSAVIA, 2003.
  8. Kuravskij L. S., Baranov S. N., Jur'ev G. A. Sintez i identifikacija skrytyh markovskih modelej dlja diagnostiki ustalostnogo razrushenija // Nejrokomp'jutery: razrabotka i primenenie. 2010. № 12. S. 20–36.
  9. Kuravskij L. S., Ushakov D. V., Marmaljuk P. A., Panfilova A. S. Issledovanie faktornyh vlijanij na razvitie psihologicheskih harakteristik s primeneniem novogo podhoda k ocenke adekvatnosti modelej nabljudenijam // Informacionnye tehnologii. 2011. № 11 (v pechati).
  10. Kuravskij L. S., Jur'ev G. A. Adaptivnoe testirovanie kak markovskij process: modeli i ih identifikacija // Nejrokomp'jutery: razrabotka i primenenie. 2011 а. № 2. S. 21–29.
  11. Kuravskij L. S., Jur'ev G. A. Ispol'zovanie markovskih modelej pri obrabotke rezul'tatov testirovanija // Voprosy psihologii. 2011 b. № 2. S. 98–107.
  12. Ovcharov L. A. Prikladnye zadachi teorii massovogo obsluzhivanija. M.: Mashinostroenie. 1969.
  13. Saati T. L. Jelementy teorii massovogo obsluzhivanija i ee prilozhenija. M.: LIBROKOM, 2010.
  14. Tihonov V. I., Shahtarin B. I., Sizyh V. V. Sluchajnye processy. Primery i zadachi. T. 5.
  15. Ocenka signalov, ih parametrov i spektrov. Osnovy teorii informacii. M.: Gorjachaja linija–Telekom, 2009.
  16. Tjumeneva Ju. A. Psihologicheskoe izmerenie. M.: Aspekt-Press, 2007.
  17. Shahtarin B. I. Sluchajnye processy v radiotehnike. T. 1. Linejnye preobrazovanija. M.: Gorjachaja linija– Telekom, 2010.
  18. Baker F. B. The Basics of Item Response Theory. ERIC Clearinghouse on Assessment and Evaluation, University of Maryland, College Park, MD, 2001.
  19. Gregory R. J. Psychological testing: History, principles, and applications (5th edition). N.Y.: Pearson, 2007.
  20. Gulliksen H. Theory of Mental Tests. John Wiley & Sons Inc, 1950.
  21. Kuravsky L. S., Malykh S. B. Application of Markov models for analysis of development of psychological characteristics // Australian Journal of Educational & Developmental Psychology. 2004. V. 2. P. 29–40.
  22. Kuravsky L. S., Baranov S. N. Condition monitoring of the structures suffered acoustic fatigue failure and forecasting their service life // Proc. Condition Monitoring 2003, Oxford, United Kingdom. P. 256–279, July 2003.
  23. Kuravsky L. S., Baranov S. N. Neural networks in fatigue damage recognition: diagnostics and statistical analysis // Proc. 11th International Congress on Sound and Vibration, St.-Petersburg, Russia. P. 2929– 2944, July 2004.
  24. Kuravsky L. S., Baranov S. N. The concept of multifactor Markov networks and its application to forecasting and diagnostics of technical systems // Proc. Condition Monitoring 2005, Cambridge, United Kingdom. P. 111–117, July 2005.
  25. Kuravsky L. S., Baranov S. N., Yuryev G. A. Synthesis and identification of hidden Markov models based on a novel statistical technique in condition monitoring // Proc. 7th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Stratford-upon-Avon, England, June 2010.
  26. Kuravsky L. S., Marmalyuk P .A., Panfilova A. S. Estimation of goodness-of-fit measures for identification of unrestricted factor models employing arbitrarily distributed observed data // Proc. 8th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Cardiff, UK, June 2011.
  27. Rasch G. Probabilistic models for some intelligence and attainment tests // Copenhagen, Danish Institute for Educational Research / Expanded edition with foreword and afterword by B. D. Wright. Chicago: The University of Chicago Press. 1980.
  28. Roweis S., Ghahramani Z. A unifying review of linear Gaussian models // Neural Computation. V. 11. № 2. 1999. P. 305–345.
  29. Wright B. D., Masters G. N. Rating scale analysis. Rasch measurements. Chicago: MESA Press, 1982.
  30. Wright B. D., Stone M. N. Best Test Design. Chicago: MESA Press, 1979.

Information About the Authors

Lev S. Kuravsky, Doctor of Engineering, professor, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-3375-8446, e-mail: l.s.kuravsky@gmail.com

Grigory A. Yuryev, PhD in Physics and Matematics, Associate Professor, Head of Department of the Computer Science Faculty, Leading Researcher, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-2960-6562, e-mail: g.a.yuryev@gmail.com

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