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Learning Indicators

Latent trait estimation

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.

References:
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. http://link.springer.com/chapter/10.1007/978-3-031-10522-7_40


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


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