In computerized tutoring, the pace of instruction is related to the student's mastery levels of the learning objectives. The observable student's behavior that can be used to measure his knowledge is usually his responses to test items. Unobservable variables that are related to learner's motivation can affect learning but are difficult to quantify. In comparison with other decision-theoretic tutoring systems, the novelties of this research are: (1) the efficiency-centric approach to develop the Bayesian networks; (2) the formulation of utility values for different tutoring outcomes that are independent of past actions and to satisfy the separability condition; (3) the development of a common measure for student's mastery levels and item difficulties; and (4) the generation of optimal policies in polynomial time. A prototype web-based tutoring system, known as iTutor, incorporating the novelties has been developed for engineering mechanics. Formative evaluations of iTutor have shown encouraging results.

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Newtonian mechanics is a core module in technology courses, but is difficult for many students to learn. Computerized tutoring can assist the teachers to provide individualized instruction. This article presents the application of decision theory to develop a tutoring system, iTutor, to select optimal tutoring actions under uncertainty of students' mastery states. The novelties of this research are: 1) the automation of student diagnosis that is made possible when tutoring alternatives and the utilities for different outcomes are incorporated to the Bayesian network; and 2) the ability of the tutoring system to select test items with difficulties that are appropriate for the students. The results from formative evaluation on iTutor indicate that it is adaptive, working in a well-structured knowledge space, and able to use the information gathered from the student's responses to dynamically modify the presentation in clearly defined ways.

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