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

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Competencies and skills as educational objectives

We can reinterpret the taxonomies of educational objectives in the light of knowledge representation3. The taxonomies of objectives in the cognitive domain Bloom 1956 and in the affective domain Krahtwohl et al 1964 have had a large influence on educational research and practice. From our viewpoint, they identify intellectual skills such as memorization, understanding, application, analysis, synthesis, and evaluation and also attitudes and values related to learning.

These authors’ intentions were to define operational training objectives to help to monitor their acquisition and assessment. Romiszowski [1981] has proposed a definition of skills more in line with action theory and cognitive science. Skills are « intellectual or physical actions, or even reactions, that a person produces in a competent manner to reach a goal. To do so, knowledge stored in memory is used (...). Any skill may be composed of four activities: perception, planning, prerequisite knowledge recall, and finally, execution of the action (performance) ».4

Another interesting aspect of the classification of skills proposed by Romiszowski is the integration of cognitive, affective, social and psychomotor skills. Rather than categorizing skills according to the type of individual response to a stimulus (new knowledge, affective attitudes, social behavior or motor actions), Romiszowski characterizes them according to their functions in the information processing cycle. He presents a four-phase cycle of skills with twelve sub-skills. As we can see on table 1, this approach can be related to the work of Polya 1957 that describes the problem solving process through phases like “understand the problem”, “generate a solution plan”, “execute the plan”, “assess and generalize the solution”.




(External stimuli reception)


Capability to concentrate on a task

Perceptual acuteness

Capability to recognize the stimulus


Capability to recognize the stimulus among other similar ones

Recall from memory

(Internal memory operations)


Knowledge of the stimulus language

Procedure recall

Presence of an adequate algorithm in memory

Schema recall

Presence of relevant concepts and principles in memory


(Internal processing operations)


Capability to restructure the problem


Capability to generate alternative solutions


Capability to assess alternate implications


(External expression operations)


Capability to make decisions and act accordingly


Capability to carry through with action


Capability to self-adapt and self-correct

Table 1 – An skill’s taxonomy in the field of education Romiszowski 1981

1.3 Skills as generic problem solving processes

The notion of generic problems or tasks was already present in one of the first reference books about expert systems [Hayes-Roth et al, 1983]; in this work, we find a first classification of generic problems into ten categories. In other pioneering studies on generic tasks, [Chandrasekaran 1987] describes these through a problem description and a resolution method, a specific algorithm. It introduces the idea of combining a small number of generic methods to solve large classes of more complex problems. Other work on generic problems [McDermott 1988], and the « components of  expertise » approach [Steels 1990] must also be mentioned.

The KADS method [Scheiber et al, 1993; Breuker and Van de Velde, 1994] is a synthesis of these studies. It actually constitutes one of the most complete methodologies encompassing knowledge acquisition for expert systems, but also project management, organizational analysis and software engineering. In KADS, an engineering software project materializes by building seven models. Four of them are of interest here: the « domain model », the « inference model », the « task model » and the « strategic model ».

In the « inference model », we find a decomposition of the generic task in a task tree. Inference diagrams are associated to the leaves of the task tree. The « task model » provides algorithmic control principles, rules to manage the tasks tree. The « strategic model », hardly developed in KADS, corresponds to heuristic principles that guide tasks execution. Together, these three models correspond to the notion of generic process, applicable to various application « domain model », another of the seven KADS models.

A generic problem is characterized by one or several goals or products to achieve; initial data and a number of operations leading to the transformation of data into results or goals. The KADS method defines eight classes of generic problems, presented in table 2.

Generic task

Generic problem

Input (data)

Products (goal)


Determine an object’s category

Classes hierarchy; Object’s attributes

Classes containing the object


Determine the cause of the problem

Symptoms, system’s component model

Defective components


Determine the future state of the system

System’s components; attributes that will vary

States: classes of the system’s possible instances


Determine a deviation class between a system’s instance and another which is said to be normal

System’s components and attributes; normal instances

Classes grouping instances according to the difference with the norm


Modify a system’s component so it is in working order

System’s model; maintenance standards

Modified model


Break down the task into interrelated steps

Deliverables, sub-tasks, time constraints

Process: sequence of tasks, input and output


Build an object (artifact)

Artifact properties, constraints to be met

Object model


Build a behavioral system model

Goals, constraints, components, viewpoints.

Model of a system’s processes and evolution strategies

Table 2 – Generic problems and tasks in software and cognitive engineering

To each generic problem corresponds a generic task, which is a generic procedure with input and results as indicated in table 2. Breaking down this generic procedure into sub-procedures results in the KADS method tasks tree. After a number of levels, terminal-level tasks (the « leaves ») are reached, to which the KADS method associates an inference schema that completes the « task model ».

Similarly, a skill can be broken down into sub-tasks, which are other skills. Each skill also has its input and products, which is a type of knowledge resulting from applying the skill. For instance, «diagnose a component system » has an input, a component system (a “part-of” hierarchy), and generates a list of faulty components. Such a process is generic in the sense that it applies to types of knowledge in many domains, rather than to knowledge in a particular domain. Also, these skills may be compared with one another through specialization links: « diagnose a car breakdown » is a kind of diagnosis skill. In sum, skills can be represented as generic or meta-processes.

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