Abstract
In this research Markov decision processes are used in order to represent lectures. The best teaching strategy computed in previous research maximizes the effectiveness of a lecture. The best teaching strategy computed in this research maximizes the effectiveness of a subject which is composed of multiple lectures. We propose two computation methods based on statistical decision theory for teaching strategies. Dynamic programming algorithm is used in the proposed methods. The first proposed method selects teaching materials for a learner. The second proposed method selects teaching materials for multiple learners. A purpose of education is represented by the reward function of Markov decision processes. A teaching strategy is represented by the policy of Markov decision processes. The best teaching strategies computed by the proposed methods maximize the expected reward of Markov decision processes. We show the effectiveness of the proposed methods by some numerical calculation examples.
