In order to help learners acquire strong knowledge structures or mental models of performance and systems, they must be exposed to instructional messages and environments which convey the complexity of the models they are intended to assimilate. Many instructional strategies and media focus on helping learners build and develop these mental models. However, before strategies and media can be selected, instructional designers must analyze content into structures which are similar to the mental models the learner will build.

Key to analyzing and building these structures is identifying the relationships between systems, environments and performance. The common structures for defining these relationships are task and learning objective statements. These structures identify a performance requirement for a system within a given environment. These statements, by their very nature though, are static and discrete and do not encompass all the dynamic relationships which exist in real-world operation.

Modeling approaches can be used to help instructional designers develop strong and complete knowledge structures of content. The very essence of modeling helps identify key relationships between systems and the environments they exist within. Unfortunately, modeling methods are not commonly associated with traditional instructional systems design. Rather, modeling skill sets are more associated with systems analysis, intelligent tutoring and computer science. This paper identifies and introduces modeling methods for instructional designers to capture the relevant performance, systems, and environmental knowledge for representations in training.