What are domain characteristics?

Domain characteristics refers to the particularities of the domain being modeled. Domains differ (apart from the content) on many aspects. To start with we can be a broad distinction between:

• Conceptual domains. These domains contain: information of principles, concepts and facts related to the (class of) system(s) being simulated; examples are harmonic oscillation, the concept of acceleration, or the function of a specific system component.
• Operational domains. These domains cover sequences of cognitive and/or noncognitive operations (procedures) that can be applied to the (class of) simulated system(s); examples are how to perform a titration, how to handle a nuclear power station emergency, or how to localise a fault in some type of equipment. Operational information per se, has less value than when this information is coupled to the related conceptual information (for example when the information to operate a power plant is coupled to information of the functioning of the plant).

In SimQuest we concentrate on conceptual models. Also, conceptual models show large variations. Model characteristic include:

• Qualitative vs. quantitative models, continuous vs. discrete and static vs. dynamic models;
• The distance between the theoretical variables and the variables that can be manipulated by the learner;
• The overall structure of the model that determines the importance of each individual variable;
• The complexity, this essentially is the number of variables and relations. Complexity is determined by the following factors:
  • Number of variables in the model. The more variables there are the more complex the model is.
  • Number of abstraction levels of the variables. When variables are dispersed over a large number of abstraction levels the model is more complex.
  • Number of values that can be given to each variable.
  • Number of relations present in the model
  • Mean number of variables in a relation. Some relations include more than two variables, for example when intermediate variables are involved. Relations that include more than two variables are clearly more complex than relation with only two variables.
  • Presence and number of conditions. Conditions put a restriction to the truth range of a relation (A relates to B, only if ...).
• The presence of different perspectives in the model;
• The difficulty of the model. This is not only related to complexity, but some relations are experienced as more difficult than others. The complexity of a domain may be influenced by a number of factors. t is not clear, however, how the different factors above add up to a single complexity level and what the contribution of each of the factors is. Also, irrespective of the number of relations, some type of relations may be experienced as more difficult than others.

Many of these characteristics have implications for the design of the learning environments.