is not possible, and landing gear situations, in which space-splitting is possible.
Annex Figure 4–1 depicts how one changes belief after observing the following actions in three separate situations from the canopy/no-split class: one redundant and one irrelevant action (both ineffectual troubleshooting moves) and one remove-and-replace action (serviceable but inefficient). Serial elimination would have been the best strategy in such cases, and is most likely to be applied when the student has strong knowledge of this strategy and all relevant subsystems. Remove-and-replace is more likely when a student possesses some subsystem knowledge but lacks familiarity with serial elimination. Weak subsystem knowledge increases chances of irrelevant and redundant actions. It is possible to get any of these classes of actions from a trainee with any combination of values of student-model variables; sometimes students with good understanding carry out redundant tests, for example, and sometimes students who lack understanding unwittingly take the same action an expert would. These possibilities must be reflected in the conditional probabilities of actions, given the values of student-model variables.
The grain size and the nature of a student model in an intelligent tutoring system should be compatible with the instructional options available (Kieras, 1988). The subsystem and strategy student-model variables in HYDRIVE summarize patterns in trouble shooting solutions at the level addressed by the intelligent tutoring system’s instruction. As a result of the three aforementioned inexpert canopy actions, Annex Figure 4–1 shows belief shifted toward lower values for serial elimination and for all subsystem variables directly involved in the situation—mechanical, hydraulic, and canopy knowledge. Any or all of these variables could be a problem, since all are required for a high likelihood of expert action. Values for subsystem variables not directly involved in the situation are also lower because, to varying degrees, students familiar with one subsystem tend to be familiar with others, and, to a lesser extent, students familiar with subsystems tend to be familiar with troubleshooting strategies. These relationships are expressed by means of the more generalized system and strategy knowledge variables at the left of the figure. These variables take advantage of the indirect information about aspects of knowledge that a given problem does not address directly, and they summarize more broadly construed aspects of proficiency that are useful in evaluation and problem selection.