IDENTIDAD MASCULINA

Knowledge engineering was developed in stride with the applications of expert systems and artificial intelligence over the last thirty years. The first systems that qualified as expert tackled complex fields of human expertise in order to create computer systems that rendered this expertise available to a greater number of people than before.

The first expert system, DENDRAL, which was developed in the field of chemistry, used a significant knowledge base focused on analysis of data col- lected by a mass spectrograph. As this extremely complex knowledge was often poorly understood and applied and never indexed, it was necessary to extract it from human experts and imitate their behavior in the selection and treat- ment of relevant knowledge. Thereafter other expert systems were developed, in fields such as medical diagnosis (MYCIN), symbolic calculation (MAC- SYMA), and the relationships between mineral deposits (PROSPECTOR). This knowledge engineering approach was also applied to various types of problems, such as predicting consequences from a description of the situa- tion, planning a series of operations, and monitoring energy power stations to identify failures or to prescribe corrective measures.23

Knowledge engineering has also been applied in education to diagnose students’ deficiencies and suggest strategies to resolve them. These intelligent tutorial systems24_{are in fact composed of three expert systems: the first}

offers a diagnosis to the student solving a problem of this type, and the third uses tutorial strategies to offer appropriate assistance to the student.

Another application of the expert systems in education directly targets expertise in instructional design.25_{These expert systems have the designer}

play the role of the student in an intelligent tutorial system. They contain knowledge of instructional design that enables them, for example, to auto- matically build educational software from the designer’s specifications for material content and shape.

During the 1990s, expert systems increasingly took the shape of compo- nents integrated into other computer systems, bringing their hosts intelligence by integrating expertise in the form of knowledge base. Thus we speak of knowledge-based systems,26_{advisers, or intelligent agents. As indicated in Table}

3-1, the introduction of expert knowledge into computer systems represents a significant evolution at the core of what we now call knowledge management. The terms data, information,andknowledgelabel three stages in the evolu- tion of computer systems. At the beginning, the large computerized manage- ment systems processed great quantities of numeric and symbolic data. These data were stored in predetermined and fixed formats built by a computer specialist from the information provided by a manager. At the beginning of

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 6 7

Table 3-1. Three Generations of Computer Systems.

Level Method Tools Actors

1 Data Central management Computer analysts, management systems for standard managers

format data

2 Information Relational database Computer architects, management systems content experts

3 Knowledge Knowledge-based Knowledge engineers, management expert system shells computer experts,

the 1980s, we became more and more aware of the information contained in the relationships between various data, and we started talking about informa- tion processing.The relational databases built by computer architects and con- taining the data provided by a content expert made it possible to dynamically vary the formats of data, relying on links between the tables storing various components of the data. However, the information that resulted remained lim- ited to factual knowledge.

The passage to knowledge processing now makes it possible to manage, in an integrated fashion, not only factual knowledge (data and information) but also more abstract knowledge describing the concepts, procedures, rules, meth- ods, know-how, and models of interest to a group of people or an organiza- tion. Knowledge acquisition, modeling, processing, and communication then become essential processes for building an organizational memory, and for developing methods to exploit it and thus render available to the users means of improving their knowledge or making that knowledge available to others.

In this context a new track appears, that of knowledge engineer, and a new discipline, knowledge engineering, which studies the methods and prac- tices for developing knowledge-based systems. The notion of knowledge representation and modeling, which will be covered later on, occupies a sig- nificant place in these methods.

Knowledge engineering implies operations like identifying knowledge, explaining it, representing it, and translating it into a symbolic or graphic lan- guage in order to facilitate its subsequent use. In a typical situation the knowl- edge engineer discusses this knowledge with one or many content experts who have the expertise he or she wishes to model. Using systematic interview meth- ods designed for the acquisition of knowledge, the engineer gradually refines a representation of the field, by iterative stages, until he or she captures it in a synthetic form, which will generally be integrated in a computer system.

However, knowledge engineering is also useful independently of this last stage as it facilitates an explicit and structured view of knowledge that can then be communicated directly to users or used as a basis for instructional engineering of a learning system. That is, knowledge engineering can be inte- grated into an instructional engineering method, as it is in the method dis- cussed in this book. The knowledge engineering processes are adapted and

specialized to help designers define the contents, the instructional scenarios, the instructional materials, and the delivery models of the learning system.

On another level, knowledge engineering can also help in defining the instructional engineering method itself. By applying one form or another of knowledge engineering throughout this book, I am identifying the concepts, processes, and principles of instructional engineering. The sources of expertise are the theories and instructional design models of educational sciences and the concepts, processes, and principles of software and knowledge engineering.