Adapting the amount of instruction to individual student needs

The purpose of this paper is to formulate optimal sequential decision rules for adapting the appropriate amount of instruction to learning needs. The framework for the approach is derived from Bayesian decision theory. It is assumed that three actions (namely master, partial master, and nonmaster) are open to the decision‐maker. The procedures are demonstrated for the problem of determining the optimal number of interrogatory examples for concept learning. It is shown that the optimal sequential decision rules take into account improvements in learning. An empirical example of computer‐based instructional decision making for concept learning in medicine concludes the paper.