Located at the crossroads of instructional design, software engineering, and knowledge engineering (Figure 3-1), from all of which it inherits its ma- jor properties, instructional engineering is a particular systemic method in the field of educational problem solving. Instructional engineering is founded on the science of systems, which defines the concept of a system as a series of units in dynamic interaction, organized in order to achieve specific goals.1
The method I am defining here aims to design a series of instructional objects to be built. It includes tasks and operational principles organized to support the creation of a learning system. The latter is a system itself, used by
learners and other participants at the moment of delivery. In other words, instructional engineering is a system that aims to develop other systems: learn- ing systems.
Instructional engineering is first and foremost a complex process of prob- lem solving as defined in cognitive science2and is sometimes studied as such
in educational sciences.3
The problems of instructional engineering are first general problems, then design problems (similar to those in architecture or mechanical engineering), then problems of instructional design, and finally, problems of instructional design calling upon the pedagogies of one or many disciplines. The study of each one of these four levels of problems generates a useful framework for the creation of an instructional engineering method.
The general cognitive science model of problem solving leads me to char- acterize the process of instructional engineering in the following terms:
• Identify a final state: here the learning system to be built, as defined in the previous chapter.
F o u n d a t i o n s o f I n s t r u c t i o n a l E n g i n e e r i n g 5 7
Figure 3-1. Foundations of Instructional Engineering.
Instructional Design Instructional Engineering Knowledge Engineering Software Engineering
• Identify an initial state: a more or less precise definition of the train- ing problem the learning system must solve.
• Identify operators, or processes: these will allow the transformation of the initial definition of the problem into increasingly precise descriptions until a concrete system is produced.
The systemic approach provides a general methodology to solve complex problems, inspired by the scientific approach and precursory work like Polya’s in the field of mathematics didactics.4It is applied to various problems in economy, architecture, education, and product design. The systemic approach identifies five main phases or processes through which problem-solving activ- ity evolves, guided by methodological rules. These phases are
• The definition of the problem,which means the most precise identifi- cation possible of the characteristics and constraints of the solution sought (the final state) and the data or the current situation (the ini- tial state).
• The analysis of the problem,which generates possible alternatives for the development of a solution.
• The development of a solution plan,which identifies the operations, the stages, the phases, or the means by which the current situation could be transformed in order to reach the final state.
• The application or the implementation of the solution plan, which involves assembling the elements of the plan to produce situations that are increasingly close to the final state.
• The evaluation of the solution and the revision,which means, on one hand, verifying that the solution obtained corresponds to the solution sought, and on the other hand, examining the solution obtained in order to reuse it to solve other problems.
Polya qualifies this problem-solving process as “heuristic,”5which means
it is guided by methodological principles that do not guarantee success but that offer “good” leads toward the solution. Such principles can be framed as
questions we may ask at each phase. For example, at the problem definition phase we may ask, What is the goal? What are we looking for exactly? Can the unknown be broken down into various units? And which units? What are the available data for the problem? At the analysis phase we may also ask, What conditions or relationships exist between the data and the unknown that would make it possible to pass from one to the other? And at the plan development phase we may ask, Can the problem be broken down into sim- pler problems? Do we have a solution for an analogous problem? Must the plan process data by successive approximations toward the unknown or, con- versely, start from the goal and use regressive reasoning?
This general methodology is useful for instructional engineering because it invites us as designers to deconstruct the training problems, which are sel- dom simple, into more accessible problems, those for which a solution may be imagined more quickly and then combined with other partial solutions. It also invites us to distinguish between the plan developed (here an instructional model or course syllabus) and its implementation, that is, the production of instructional material, the choice of media supports, and the integration of tools and means of communication. Last of all it invites us to plan an eval- uation and revision phase before the implementation of the learning system.
The design problems are similar whether we work in architecture or in physical, software, or instructional engineering. In all cases the solution is the creation of a system that deals with certain constraints that are barely defined at the start and that must be specified in the initial phase and then made more specific throughout the process. After observing engineers working on design problems of various types, Goël and Pirolli identified a certain number of invariable types of strategic knowledge used during the problem-solving process,6and their findings relate to the complexity of the instructional design
problem.
• The designers engage in intense activity in structuring and restruc- turing the problem.
• The designers develop several system models, in plans, functional dia- grams, and prototypes.
• The fact that there are no “good” or “bad” answers leads to the con- tinual evaluation of the value of a solution or of a component of the solution.
• This evaluation is iterative and carried out by successive approximations. • The designers tend to gradually specify the contours of the system
while trying to preserve a certain latitude.
• The designers deconstruct the problem into permeable modules hav- ing intersections and links that are more or less elaborate.
• The designers move from the initial abstract goals toward the final concrete specifications by a series of increasingly precise approxima- tions, until they produce the system that constitutes the solution to the initial problem.
• The designers use symbolic and graphic systems abundantly to describe the intermediate results.
The MISA method presented in Chapter Four was inspired largely by this systemic analysis of design problems. These principles also apply to the three methodologies from which MISA inherits some of its properties: instructional design, software engineering, and knowledge engineering.