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

The random-trial incremental (RTI) model of human associative learning prop oses that learning due to a trial where the association is presented procee ds incrementally, but with a certain probability, constant across trials, n o learning occurs due to a trial. Based on RTI, identifying a policy for se quencing presentation trials of different associations for maximizing overa ll learning can be accomplished via a factored Markov decision proces (MDP) . For both finite and infinite horizons and a quite general structure of co sts and rewards, a policy that on each trial presents an association that l eads to the maximum expected immediate net reward is optimal.