Knowledge Models and Cognitive Structure

Although instructional models permit the evaluation of a learner’s progress, they do not indicate whether the learner has acquired the target knowledge and skills. To learn this, we need to use the knowledge model.

In MISA, the knowledge model for a learning event (a program, course, or activity) is built gradually. In ED 212, the designer first builds a prelimi- nary general model of the contents of the instructional event, then in ED 310, the designer develops this model by producing a submodel for the con- tent of each learning unit, and finally, in ED 410, he or she further refines the general model by producing a knowledge submodel for each instrument in the learning scenarios of the learning units. Figure 5-8 shows the knowl- edge submodel for a learning unit.

I n s t r u c t i o n a l E n g i n e e r i n g o n t h e We b 1 3 9

The cognitive structure that follows is the knowledge model for the ped- agogical structure shown in the previous box. It demonstrates that the knowl- edge model for an instructional structure is not necessarily unique to that structure nor is it necessarily an architectural match for that structure. For example, a subject like “structured representation” may occur in more than one learning unit, especially in spiral models of instruction or in project-based learning. Or a course may be subdivided into three instructional units—a presentation, a project, and a group discussion—each one covering the entire content of the knowledge model.

C O G N I T I V E S T R U C T U R E ( C S ) F O R T H E C O U R S E “A R T I F I C I A L I N T E L L I G E N C E ”

Inferences and Reasoning Processes*

State-operators models* Knowledge processing* Multiagent systems* Knowledge Representation Expert systems Propositional logic Predicate logic Structured representation Object representation Programming paradigms

Natural Language Processing

Sentence analysis Generative grammar Semantics

Learning and Distribution

Machine learning Neuron networks Multiagent systems*

Social Impact of AI

AI and education AI, work, and economy AI, culture, and society

The passage from the main knowledge model and its submodels in MISA to the cognitive structure that the designer has created earlier in Explor@ is not as direct as in building an instructional structure. In the former case, the designer examines only the principalknowledge, that for which target com- petencies will be defined and for which learning evaluations will be carried out. The asterisks (*) in the cognitive structure displayed here indicate the principal knowledge units, those labeled “P” on the knowledge submodel shown in Figure 5-8. (This submodel of course displays only part of the com- plete cognitive structure.)

In addition, along with the instructional structure, the cognitive struc- ture assists in defining progress levels in the acquisition of knowledge and cri- teria for assessing the learning level achieved at a given time. As discussed in Chapter Two, we can define a user model as composed of progress levels for the activities achieved according to the instructional model and the knowl- edge and competencies acquired according to the knowledge model and the cognitive structure. This user model allows Explor@ to launch various “intel- ligent” tools, such as knowledge and competencies assessments or group pro- files for the trainer. These tools can then be integrated in the Explor@ environments according to the roles played by each actor.

S U M M A R Y

In contrast to most proprietary systems, which are limited to the production of courseware on the Web, MISA and its support system, ADISA, allow the construction of large-scale learning systems that integrate several courses, each one composed of several activities, documents, or resources, using a variety of media formats.

After this brief overview of instructional engineering, readers may feel that the topic is highly complex. It is true that instructional engineering overall is not an easy task; however, it can be simplified though the use of integrated tools such as ADISA and the application of well-chosen instructional engi- neering operations and principles. Moreover, this impression of complexity comes mainly from the fact that the method discussed here is general. It was I n s t r u c t i o n a l E n g i n e e r i n g o n t h e We b 1 4 1

designed so it could be applied to multiple and extremely diverse situations. My experience has been that each single project carried out with MISA and ADISA has used only a fraction of the functions available. In other words, individual projects are highly unlikely to have this level of complexity. This is demonstrated through the case studies presented in Chapters Six, Seven, and Eight.

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T

describes how the instructional engineering method MISA 4.0 was applied to transform a university course so that it could be delivered through a Web site in a virtual learning center. The course Inf-5100, Introduction to Artificial Intelligence, is taught in three Télé-université programs. It was offered for the first time in 1990, and has achieved a certain success because over two thousand students have registered in the following years. From 1990 to 1999, the course underwent two minor revisions. At the beginning of 1999, it was reengineered to update the contents, to integrate new collabo- rative activities, and to provide Internet delivery of materials and on-line tutoring. The design of the new version of the course began in April 1999 and was completed by November 1999. The course has been offered in this Web version since these modifications took place.

6

Reengineering a University