We will now illustrate a method to guide the modeling of a knowledge domain..
For this, we need the concept of learning need. The identification of learning needs is an important element of the initial phases of analysis and design of a learning system. Learning need is a meta-concept applicable in all the domains of knowledge and it can be defined precisely in the meta-knowledge domain. 13 It rests on an evaluation of the distance between the current competency of a learner or a group of learners and a target competency to be achieved at the end of the learning process.
Target competencies are found in competency profiles as discussed above. So, if one wants to propose a plan for training persons to become multimedia directors, it is necessary to estimate the distance between its current competencies and the ones defined in a typical profile for a multimedia director.
To estimate this distance, we can use a scale to evaluate the progress in the acquisition of a competency. In MISA, we use such a scale to associate to every competence of an actor (skill + knowledge) a value between 0 and 10 subdivided into four levels: sensitization [from 0 to 2,5], familiarization (from 2,5 to 5], mastery (from 5 to 7,5] and expertise (from 7,5 to 10].
Figure 8 – Learning needs for an actor (multimedia director) on a competency scale
Figure 8 presents a progress scale for the competencies of an actor, for example a multimedia director. Competence A, " To build a production method ", presents a rather important gap (around 4,2), because the multimedia director has to master this competence (5,8), while the persons for whom training is intended have reached on average a simple sensitization level (1,6). On the other hand, competency B, " To discriminate between the properties of audio-visual aids ", presents a smaller learning need of about 1.6, representing a progress from one level of familiarization to the other.
MISA proposes to guide knowledge modeling in a domain by means of elaboration principles similar to elaboration theory Reigeluth 1983. We develop a model by successive levels taking into account the learning needs assigned to so-called "principal knowledge". A principal knowledge is one to which a skill and a competency have been assigned in a model, thus enabling the evaluation of learning needs. We then apply the following heuristic principles.
A knowledge for which the learning need is large (for at least one group of learners) will deserve to be clarified by indicating some of its components, inputs and products, and its regulating principles. It will be developed into a sub-model on possibly many levels until we reach a state where the learning needs are small for each knowledge unit (no new knowledge can become principal). On the contrary, if the learning need is very small for a principal knowledge, it can be removed from the model unless it serves a clarification purpose towards other knowledge units around it.
To define learning needs and guide the modeling process, we need to assign skills to knowledge using the A link. Here are some skill selection principles based on the second layer of the skills taxonomy.
If a knowledge is fundamental to actors in the target population and these actors have to reach an advanced level of expertise to be able to advise other persons, the level of skill should be high: 7-Repair , 8-Synthesize , 9-Evaluate or 10-Self-manage.
If a knowledge is important to actors in the target population, requiring from them a large level of autonomy, the level of skill should be above average: 5-Apply/Use, 6-Analyze , 7-Repair, 8-Synthesize.
If a knowledge is useful to actors in the target population, requiring its regular use, the level of skill should be average: 3-Instanciate , 4-Transpose , 5- Apply/Use or 6-Analyse.
If a knowledge is sometimes useful to actors in the target population, asking them to retain only the main elements, the level of skill can be weak: 1-Pay attention, 2-Integrate or 3-Instanciate.