Optimal Sequential Rules for Computer-Based Instruction

Abstract

The purpose of this article is to formulate sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. The framework for the approach is derived from Bayesian decision theory. Both a threshold and linear utility structure are considered. The binomial distribution as well as Kelley's regression line from classical test theory are adopted as the psychometric model involved. Optimal sequential rules will be derived both for the situation that collateral information but no prior knowledge about student's true level of functioning is available and for the situation that a beta distribution representing student's prior knowledge is assumed. An empirical example of sequential instructional decision making for concept-learning in medicine concludes the article.