Domain Model
The domain model is a representation of the subject material to be
taught. Generally, this model represents some process or system in a
computationally interpretable form. This is a defining characteristic of
“symbolic AI”; such models are variously described as qualitative,
relational, and/or semantic. Clancey [7] presented evidence that all
expert systems were “model-based.” In effect, a fundamental
contribution of expert systems research has been to extend modeling
formalisms beyond mathematical constructs in conventional science and
engineering to include qualitative/relational models and associated
modeling operations [10].
By design, an ITS domain model can be interpreted to solve problems
presented to the student. This model constitutes the knowledge to be
learned: facts, formalisms, causal processes, and reasoning processes
(e.g., a diagnostic strategy). Typically, the program that applies the
domain model to solve problems is called the inference engine(e.g., a rule interpreter), which applied to particular circumstances
(e.g., a patient’s history and symptoms), produces a (partial) solution
called a situation-specific model .
At first, most ITS researchers viewed the domain model as being
isomorphic to stored structures and subconscious processes in the brain.
This assumption motivated much productive research in which the ITS
creates a model of the student’s knowledge and reasoning. In recent
decades, models have been viewed more often as scientific toolsthat are constructed and applied to understand and manipulate processes
and systems in the world [11, 21], exemplified by the student model
in an ITS.