## Abstract

This paper discusses the internal characteristics of simulations. The major part of it is concerned with models and their relation with the domain. Some central concepts regarding modelling and simulation are defined. These include concepts regarding:

- the structure and characteristics of the model;

- the relationship to the system that is being modelled;

- the interaction of the learner or other agents with the model. A classification of model types is presented, accompanied by a first idea on the representation of the several types of models. The classification includes the distinction between qualitative and quantitative models. Models can further be classified into dynamic and static models, determined by the time dependency of the model. The basic elements of any simulation model are the state of the model, describing the properties of the system that is modelled, and a set of rules determining the possible development of the model state. State space is the collection of all possible states.

In quantitative models the basic elements of the state are variables, which can be dependent or independent. Dependent variables are variables of which the value is determined by the independent variables. The model rules are equations, determining the development of the values of the variables. Quantitative models are classified into discrete and continuous models, depending on the structure of the state space. Qualitative models have a state space consisting of propositions about the modelled system. In this case, the model rules have a more descriptive character.

A brief discussion of the relationship between the model and the corresponding real system is given. Three types of real systems are distinguished: physical, artificial and abstract. The main criterion for a distinction between these types of systems is the possibility of constructing a model that describes the system completely (a base model).

The interaction of the learner with models and simulations is described by introducing the concepts of interaction and scenario. The interaction describes the sequence of operations that are performed upon the model, the scenario includes the interaction and the agents who take part in the interaction.

Classifications of instructional simulation environments (often just called: instructional (or educational) simulations) are discussed. The usefulness and features of these classifications are investigated. Many of the existing classifications do not distinguish very well between relevant aspects of simulation learning environment.

Three sections describe the relationship between the internal characteristics of simulations and the four themes introduced in de Jong (this volume): domain models, learning goals, learning processes and learner activity. Because simulation models are discussed extensively in the first section of this paper, the section on domain and simulation models gives an overview of domain aspects that are not explicitly referred to in the model. Here, an additional knowledge base, called the cognitive model will be introduced. For each type of learning goal the relation with the domain model or scenario is elaborated. The relationship between learning processes and learner activity and domain models is discussed by relating the possible types of learner activity with the model and scenario elements, resulting in demands for the structure of the model or scenario.

- the structure and characteristics of the model;

- the relationship to the system that is being modelled;

- the interaction of the learner or other agents with the model. A classification of model types is presented, accompanied by a first idea on the representation of the several types of models. The classification includes the distinction between qualitative and quantitative models. Models can further be classified into dynamic and static models, determined by the time dependency of the model. The basic elements of any simulation model are the state of the model, describing the properties of the system that is modelled, and a set of rules determining the possible development of the model state. State space is the collection of all possible states.

In quantitative models the basic elements of the state are variables, which can be dependent or independent. Dependent variables are variables of which the value is determined by the independent variables. The model rules are equations, determining the development of the values of the variables. Quantitative models are classified into discrete and continuous models, depending on the structure of the state space. Qualitative models have a state space consisting of propositions about the modelled system. In this case, the model rules have a more descriptive character.

A brief discussion of the relationship between the model and the corresponding real system is given. Three types of real systems are distinguished: physical, artificial and abstract. The main criterion for a distinction between these types of systems is the possibility of constructing a model that describes the system completely (a base model).

The interaction of the learner with models and simulations is described by introducing the concepts of interaction and scenario. The interaction describes the sequence of operations that are performed upon the model, the scenario includes the interaction and the agents who take part in the interaction.

Classifications of instructional simulation environments (often just called: instructional (or educational) simulations) are discussed. The usefulness and features of these classifications are investigated. Many of the existing classifications do not distinguish very well between relevant aspects of simulation learning environment.

Three sections describe the relationship between the internal characteristics of simulations and the four themes introduced in de Jong (this volume): domain models, learning goals, learning processes and learner activity. Because simulation models are discussed extensively in the first section of this paper, the section on domain and simulation models gives an overview of domain aspects that are not explicitly referred to in the model. Here, an additional knowledge base, called the cognitive model will be introduced. For each type of learning goal the relation with the domain model or scenario is elaborated. The relationship between learning processes and learner activity and domain models is discussed by relating the possible types of learner activity with the model and scenario elements, resulting in demands for the structure of the model or scenario.

Original language | English |
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Pages (from-to) | 241-262 |

Journal | Education & computing |

Volume | 6 |

Issue number | 3-4 |

DOIs | |

Publication status | Published - 1991 |