Katsikopoulos, K. V. (2000) Optimal instructional policies based on a random-trial incremental model of learning. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 30 (4), 490-494. (doi:10.1109/3468.852441).
Abstract
The random-trial incremental (RTI) model of human associative learning proposes that learning due to a trial where the association is presented proceeds incrementally, but with a certain probability, constant across trials, no learning occurs due to a trial. Based on RTI, identifying a policy for sequencing presentation trials of different associations for maximizing overall learning can be accomplished via a factored Markov decision process (MDP). For both finite and infinite horizons and a quite general structure of costs and rewards, a policy that on each trial presents an association that leads to the maximum expected immediate net reward is optimal.
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