|
|
Probabilistic method of filtering artifacts in adaptive testing 1049
Kuravsky L.S.
Doctor of Engineering, 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 Yur'ev G.A.
Post-graduate Student, Faculty of Information Technologies, Department of Applied Informatics, MCUPE , Moscow, Russia
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.
-
Avanesov V. S. Pedagogicheskoe izmerenie latentnyh
kachestv // Pedagogicheskaja diagnostika. 2003. № 4. S. 69–78.
-
Kardanova E. Ju. Modelirovanie i parametrizacija
testov: osnovy teorii i prilozhenija. M.: FGU «Federal'nyj centr
testirovanija», 2008.
-
Kramer G. Matematicheskie metody statistiki. M.:
Mir, 1976.
-
Kuravskij L. S., Baranov S. N. Sintez setej Markova
dlja prognozirovanija ustalostnogo razrushenija // Nejrokomp'jutery: razrabotka
i primenenie. 2002. № 11. S. 29–40.
-
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.
-
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.
-
Kuravskij L. S., Baranov S. N., Malyh S. B.
Nejronnye seti v zadachah prognozirovanija, diagnostiki i analiza dannyh:
Ucheb. posobie. M.: RUSAVIA, 2003.
-
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.
-
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).
-
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.
-
Kuravskij L. S., Jur'ev G. A. Ispol'zovanie
markovskih modelej pri obrabotke rezul'tatov testirovanija // Voprosy
psihologii. 2011 b. № 2. S. 98–107.
-
Ovcharov L. A. Prikladnye zadachi teorii massovogo
obsluzhivanija. M.: Mashinostroenie. 1969.
-
Saati T. L. Jelementy teorii massovogo
obsluzhivanija i ee prilozhenija. M.: LIBROKOM, 2010.
-
Tihonov V. I., Shahtarin B. I., Sizyh V. V.
Sluchajnye processy. Primery i zadachi. T. 5.
-
Ocenka signalov, ih parametrov i spektrov. Osnovy teorii
informacii. M.: Gorjachaja linija–Telekom, 2009.
-
Tjumeneva Ju. A. Psihologicheskoe izmerenie. M.:
Aspekt-Press, 2007.
-
Shahtarin B. I. Sluchajnye processy v radiotehnike.
T. 1. Linejnye preobrazovanija. M.: Gorjachaja linija– Telekom, 2010.
-
Baker F. B. The Basics of Item Response Theory. ERIC
Clearinghouse on Assessment and Evaluation, University of Maryland, College
Park, MD, 2001.
-
Gregory R. J. Psychological testing: History,
principles, and applications (5th edition). N.Y.: Pearson, 2007.
-
Gulliksen H. Theory of Mental Tests. John Wiley &
Sons Inc, 1950.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
Roweis S., Ghahramani Z. A unifying review of linear
Gaussian models // Neural Computation. V. 11. № 2. 1999. P. 305–345.
-
Wright B. D., Masters G. N. Rating scale analysis.
Rasch measurements. Chicago: MESA Press, 1982.
-
Wright B. D., Stone M. N. Best Test Design. Chicago:
MESA Press, 1979.
|
|
