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Learning Indicators

Latent trait estimation

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It is based on the item response theory, and the sequential decisions are performed by designing special cases or generalizations of Markov Decision Processes (in particular, a Stochastic Shortest Path problem or a Partially Observable Markov Decision Process) as to be able to model different Computerized Adaptive Testing formalisms, in which questions are put forward by the examiner one at a time to the student.

Importance of the indicator for the students’ learning methodology:
The estimation of the latent trait can be done with a Bayesian estimator or with a Maximum Likelihood estimator, given a sequence of answers to several questions put forward by the examiner. An optimal examiner can be obtained by solving a constrained optimization problem. As the latent trait is sequentially estimated, the new questions are specifically taylored to the estimated knowledge of the student.

P. Gilavert, V. Freire. Computerized Adaptive Testing: A unified approach under Markov Decision Process. In O. Gervasi, B. Murgante, S. Misra, A. Rocha, C. Garau (eds.), Computational Science and Its Applications--Proceedings of the ICCSA 2022 Workshops, LNCS 13375, ISBN 9783031105210 (July 2022), pp. 591-602.

Author of the review:
Pablo Guerrero-Garcia
University of Malaga


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Eligius Hendrix

Indeed, the idea to apply a stochastic model on observations of answers to questions seems a very systematic approach. We love Markov.