Optimal instructional policies based on a random-trial incremental model of learning

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 Markov decision process. 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.