A new approach to computerized adaptive testing 1025
Doctor of Engineering, Dean of the Computer Science Faculty , Moscow State University of Psychology and Education , Moscow, Russia
PhD in Engineering, Professor of the Department of Applied Informatics and Multimedia Technologies, Head of the Center of Information Technologies for Psychological Research of the Faculty of Information Technologies, Moscow State University of Psychology and Education, Moscow, Russia
PhD in Physics and Matematics, Associate Professor, Head of Scientifi c Laboratory, Moscow State University of Psychology and Education, Moscow, Russia
Doctor of Psychology, Leading Scientist, Moscow State University of Psychology and Education, Moscow, Russia
A new approach to computerized adaptive testing is presented on the basis of discrete-state discrete-time Markov processes. This approach is based on an extension of the G. Rasch model used in the Item Response Theory (IRT) and has decisive advantages over the adaptive IRT testing. This approach has a number of competitive advantages: takes into account all the observed history of performing test items that includes the distribution of successful and unsuccessful item solutions; incorporates time spent on performing test items; forecasts results in the future behavior of the subjects; allows for self-learning and changing subject abilities during a testing procedure; contains easily available model identification procedure based on simply accessible observation data. Markov processes and the adaptive transitions between the items remain hidden for the subjects who have access to the items only and do not know all the intrinsic mathematical details of a testing procedure. The developed model of adaptive testing is easily generalized for the case of polytomous items and multidimensional items and model structures.
Testing procedures are increasingly used in many
contemporary applications requiring assessment of people or machine’s behavior.
According to conventional models of testing based on classical test theory for
measuring the examinee’s level in a specific skill or ability as preciselyas
possible these procedures usually should implement a big number of items that
makes testing difficult to use.
Burden R.L., Faires J.D. Numerical Analysis. Brooks
Cole, 2011. 895 p.
Kohonen T. Self-Organizing Maps. Springer, 2001.
Kuravsky L. S., Marmalyuk P. A., Yuryev G. A., Dumin P. N.
A Numerical Technique for the Identification of Discrete-State Continuous-Time
Markov Models. Applied Mathematical Sciences, 2015, vol. 9, no. 8, pp.
379–391. URL: https://doi.org/10.12988/ams. 2015.410882.
Kuravsky L.S., Margolis A.A., Marmalyuk P.A., Panfilova
A.S., Yuryev G.A., Dumin P.N. A Probabilistic Model of Adaptive Training.
Applied Mathematical Sciences, 2016, vol. 10, no. 48, pp. 2369—2380.
Kuravsky L.S., Margolis A.A., Marmalyuk P.A., Yuryev G.A.,
Dumin P.N. Trained Markov Models to Optimize the Order of Tasks in
Psychological Testing. Neirokomp'yutery: Razrabotka, Primenenie
[Neurocomputers: Development, Application], 2013, no. 4, pp. 28–38 (In
Kuravsky L.S., Marmalyuk P.A., Baranov S.N., Alkhimov
V.I., Yuryev G.A., Artyukhina S.V. A New Technique for Testing Professional
Skills and Competencies and Examples of its Practical Applications. Applied
Mathematical Sciences, 2015, vol. 9, no. 21, pp. 1003—1026.
Kuravsky L.S., Yuryev G.A. Primenenie markovskikh modeley
dlya testirovania resultatov [Applying Markov Models to Processing Testing
Results]. Voprosy Psychologii, 2011, no. 2, pp. 112—121, (In
Thompson N.A., Weiss D.J. A framework for the development
of computerized adaptive tests. Practical Assessment. Research &
Evaluation, 2011, vol. 16, no. 1, pp. 1—9.
Torre J. de la, Patz R.J. Making the Most of What We Have:
A Practical Application of Multidimensional Item Response Theory in Test
Scoring. Journal of Educational and Behavioral Statistics, 2005, vol.
30, no. 3, pp. 295—311. doi:10.3102/10769986030003295.
Wilkinson J.H. The Algebraic Eigenvalue Problem.
Oxford, Clarendon Press, 1988, 662 p.