Skills and Competencies as Representable Meta-knowledge for Tele-learning Design by Gilbert Paquette cirta-licef research Center

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Ordering skills from simple to complex

The question whether skills are ordered from simple to complex is not a simple one. Between, The definitions presented in table 5 supports this hypothesis for skills in the first layer: reception skills involve only attention and memory operations, reproduction skills are essentially instantiation from more general knowledge, creation skills produce new knowledge from more specialized ones and, finally, self management skills involves explicit meta-cognitive operations.

Name of skill




Input = internal or external stimulus;

Product = facts or knowledge located or stored in memory


Input = knowledge and models;

Products = facts obtained through instancing or knowledge obtained through reformulation

  • Use examples to explain or illustrate a concept, a procedure or a principle;

  • Use a model to explain facts;

  • Simulate a process.

Produce/ Create

Input = knowledge and models;

Products = new knowledge or models resulting from analysis and synthesis

  • Classify objects according to a taxonomy;

  • Repair defective system components ;

  • Plan a project;

  • Model and build a system.


Input = knowledge, models, generic facts;

Product = knowledge, models, meta- knowledge linked to domain model

  • Assess knowledge validity or self competence;

  • Initiate a change process after assessing the situation;

  • Apply a generic strategy to improve learning and performance.

Table 5 – Comparison of the more general skills, from simple to complex

On the other hand, the skills on the third layer of the taxonomy are probably not ordered from simple to complex. For example, the integrate sub-skills are simply inverse operations for simple retrieval and storage, while the four analyze sub-skills or the three synthesize sub-skills on table 3 are much on the same level of complexity.

So it seems that as we move from general to more specific layers, the skills in a layer are less likely to be ordered form simple to complex. On table 3, we have assigned numbers to the second layer. We have now to show evidence that this layer can be ordered from simple to complex.

This assertion is not evident and was sometimes disputed in the case of taxonomies presented in section 1. For example, the authors of the KADS method have preferred to put emphasis on the organization of sequences of generic tasks than of a hierarchical order among them6.

On the other hand, Bloom has insisted on the hierarchical organization between educational outcomes: “Our attempt to order the educational behavior from simple the complex is based on the idea that a given simple behavior can become integrated with another simple behavior to form a more complex behavior. Consequently, our classification can be perceived as that behavior of type A forms a class, behavior of type AB another class and behavior of type ABC still another class”. One finds a similar preoccupation in the elaboration of the taxonomy of the affective domain. “This organization of constituents seems to describe a process according to which certain phenomenon or value progress from one level of simple awareness to a level where it drives or controls the behavior of a person.”7

Experimental studies have tried to verify this hypothesis. Tests have been given in a large number of students containing questions connected to various complexity levels in both taxonomies. With this experimental setting, one should notice a bigger percentage of failure for questions related to the higher taxonomy levels.

As far as the taxonomy of the cognitive domain, according to Martin and Briggs8, some studies support to a certain extent the hypothesis of the organization of levels. The evidence is stronger in the first levels than in the more advanced levels. One finds the same kind of conclusions in the case of the taxonomy of the emotional domain, even though there are less studies to support this.

We suspect that the limited evidence coming from certain studies is due to the absence of a meta-knowledge representation scheme for skills. For example, certain studies note similar results for analysis on one hand, and for evaluation and the synthesis on the other hand. But if one distinguishes synthesis from analysis by the ascent in abstraction, and if one distinguishes evaluation both from synthesis and analysis by the use of meta-values that are properties of knowledge, it is likely that one can maintain the hypothesis of an increasing order of complexity for Bloom’s taxonomy as well as for the second layer of our taxonomy.

We will give here a clear definition of a skill’s complexity: A skill A is more complex than a skill B if the generic process representing B appears as a sub-process in the model of the generic process A.

Figure 3 gives an example of this definition. The “Simulate a process” skill (level of complexity 5) is decomposed into four sub-processes such as: produce examples of the input concept (instantiate: level 3), identify the next applicable procedure (identify: level 2), assemble the simulation trace (transpose: level 4), and finally execute the procedure using its execution principles, a specialization of the application skill (same level as simulation).

Figure 4 present another example of simple to complex ordering within the second layer of the taxonomy. A generic process for self-control and adaptation (10.2) is here presented. It starts by obtaining a description of a project or a process of change in a particular domain, as well as some criteria for success appropriate for this domain. At the beginning, the actor plans (8.2) the activities in a project and influences (10.1) the participants so that they coordinate themselves to achieve the activities of the project and perform according to the success criteria.

Afterwards, progress is constantly estimated and re-evaluated. If unforeseen problems arise, it is necessary for the actor to adapt himself to re-order the course of events, to redefine the roles of the participants or to adapt the criteria of success. The principles of control and adaptation rule the transition between sub-processes, for example by specifying when one has to estimate progress and success or when one has to adjust the criteria or end the generic process.

Periodically, the actor evaluates (9) the distance from the goal with regard to the success criteria available at a given moment. If the group is far from the goal, the actor begins to reorganize so as to increase the chances of success. He can also modify the criteria of success.

his representation of a level 10 skills shows that it is more complex that the level 8 or 9 skills that are invoked as sub-processes.

Figure 4 – A meta-process for control and adaptation

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