A probabilistic model of adaptive training

Kuravsky, L S; Марголис, А А; Marmalyuk, P A; Panfilova, A S; Yuryev, G.A.; Dumin, P.N.

doi:10.12988/ams.2016.65168

Cited by 10 publications

(7 citation statements)

References 5 publications

(7 reference statements)

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“…, where , is the probability of selecting the i th item. Since all items are being selected equiprobable, , = 1 , and…”

Section: Modeling a Testing Procedure: Main Concepts And Methodsmentioning

confidence: 99%

Probabilistic modeling of a testing procedure

Kuravsky¹,

Marmalyuk²,

Yuryev³

et al. 2015

ams

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Presented is a new approach to probabilistic modeling of a testing procedure, which allows considering the dynamics of subjects' abilities and difficulty of tasks during their solving. The modified Rasch function is used to build quantitative estimates, which arguments, unlike the classical version, are set in the probability scale. Methods of presented dependencies' identification using observational data are examined. As an illustration, the example of practical application of the proposed approach for modeling the Raven's Progressive Matrices testing procedure is given.

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“…, where , is the probability of selecting the i th item. Since all items are being selected equiprobable, , = 1 , and…”

Section: Modeling a Testing Procedure: Main Concepts And Methodsmentioning

confidence: 99%

Probabilistic modeling of a testing procedure

Kuravsky¹,

Marmalyuk²,

Yuryev³

et al. 2015

ams

Self Cite

View full text Add to dashboard Cite

show abstract

“…Probabilistic models represented by Markov random processes with discrete states and continuous time [6,[18][19][20][21][22][23][24][25]32] for each pattern cluster are created using the identification procedure to represent probabilistic dynamics for each operator skill class to forecast probabilistic class behavior. This step is implemented in two ways: via distribution of probabilities of being in model states and via dynamics of mathematical expectations for each independent parameter determined with the aid of the Principal Components Analysis (correspondingly, these parameters are considered approximately as independent ones).…”

Section: Basic Approach: Analysis Of Activity Parameters Represented mentioning

confidence: 99%

New approaches for assessing the activities of operators of complex technical systems

Kuravsky

Yuriev

Zlatomrezhev

2019

Experimental Psychology (Russia)

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Presented are new approaches for supporting the outcome grading for activities of operators of complex technical systems, which are based on comparisons of current exercises with the activity database patterns in both the wavelet representation metric associated with time series of activity parameters and the likelihood metric of eigenvalue trajectories for these parameters transforms as well as on probabilistic assessments of skill class recognition using sample distribution functions of exercise distances to cluster centers in a scaling space and Bayesian likelihood estimations with the aid of probabilistic profile of staying in activity parameter ranges. These techniques have demonstrated the capabilities of recognizing sets of abnormal exercises and detection of parameters characterizing operator mistakes to reveal the causes of abnormality. The techniques in question overcome limitations of existing methods and provide advantages over manual data analysis since they greatly reduce the combinatorial enumeration of the options considered.

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“…10, no. 3 In contrast to the approach presented in the papers [4][5][6][7], identification of the model under study does not require solving difficult optimization problems. If a relevant data base with observation results is available, it is reduced to the rather simple estimation of the following model parameters: function f(i), value a, factor k(t * i , i, d), and time limits {t * i } i=0, ... , n .…”

Section: Model Identificationmentioning

confidence: 99%

“…T. 10. № 3 4. Selected item difficulties are linked to the history of performing test items and do not depend directly on the current estimations of subjects' attainment levels.…”

Section: Introductionmentioning

confidence: 99%

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A new approach to computerized adaptive testing

Kuravsky

Artemenkov

Yuryev

et al. 2017

Experimental Psychology (Russia)

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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.

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A probabilistic model of adaptive training

Cited by 10 publications

References 5 publications

Probabilistic modeling of a testing procedure

Probabilistic modeling of a testing procedure

New approaches for assessing the activities of operators of complex technical systems

A new approach to computerized adaptive testing

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