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



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1.4 Skills as active metaknowledge


Skills, generic problems and tasks can only be interpreted in the meta-knowledge domain. Although many studies involve meta-knowledge, the term is not always explicitly used. These studies can be found in various domains such as mathematical logic [Thayse 1988], scientific methodology [Popper 1961], problem sovling and its teaching [Polya 1967], education [Romisowski 1987, Merrill 1994], software and cognitive engineering [Chandrasekaran 1987, Schreiber et al 1993], artificial intelligence [Pitrat 1991].

Pitrat, a pioneer of Artificial Intelligence in France, has produced an important synthesis in which he distinguishes several meta-knowledge categories and proposes the following definition: « meta-knowledge is knowledge about knowledge, rather than knowledge from a specific domain such as mathematics, medicine or geology. » [Pitrat 1993]. According to this definition, meta-knowledge is at the heart of the learning process, which consists in transforming information into knowledge:



  • by associating values to application knowledge such as : truth, usefulness, importance, knowledge priority, competence of an individual towards a knowledge object, etc.

  • by describing « intellectual acts » or cognitive processes facilitating knowledge processing in application domains: memorization, application, analysis, synthesis, evaluation, etc.

  • by representing strategies to acquire, process and use knowledge: memorization techniques, heuristic principles for problem solving, project management strategies, etc.

[Romisowki 1981] expresses very well the simultaneous phenomenon of knowledge acquisition in a particular domain, and the building of meta-knowledge and skills : « The learner follows two kinds of objectives at the same time - learning specific new knowledge and learning to better analyze what he already knows, to restructure knowledge, to validate new ideas and formulate new knowledge », an idea expressed in another way by Pitrat : « meta-knowledge is being created at the same time as knowledge ». When learning new knowledge, a person uses meta-knowledge (at least minimally) without necessarily being aware of it. However, using meta-knowledge should really be a learner’s conscious act. These objectives justify the inclusion of meta-knowledge within a knowledge model that represents a learning system’s contents, i.e. that provides a structured representation of « learning objects».

Jacques Pitrat define the notion of active meta-knowledge, as opposed to passive meta-knowledge (knowledge properties, knowledge about an individual’s knowledge). Active meta-knowledge is knowledge that “physically” handles other knowledge. Pitrat 1990] defines six types of active meta-knowledge:



  • Knowledge acquisition consists in examining and diagnosing available information and knowledge, completing it, if incomplete or inconsistent with other knowledge previously acquired, and reformulating it, as needed, so it may be stored in memory.

  • Knowledge discovery regroups a set of operations like instanciation, specialization or analogy, which allow transformation of acquired knowledge into new knowledge.

  • Knowledge storage consists in deciding where and how to register knowledge in a structured way in memory so it can become quickly available when needed, following the shortest association chains, without having to systematically scan memory.

  • Knowledge search is essentially a set of knowledge reconstruction operations to extract from memory the knowledge needed to solve a problem or accomplish a task.

  • Knowledge use regroups a set of operations required to apply knowledge that has been extracted or reconstructed from memory, in order to build a solution for a problem, designing and managing solution plans and results explanation.

  • K
    nowledge expression is the inverse of acquisition, to communicate acquired knowledge to another information processing system, generally a human being; these operations enable a person to choose what to say and how to say it, according to a model of the intended receiver.

Figure 1 – Relations between active meta-knowledge objects

Figure 1 shows relations between different kinds of active meta-knowledge. Discovery is the only meta-knowledge that produces new knowledge from raw data or structured information. Acquisition is the meta-knowledge that allows integration of self-discovered or externally provided knowledge. The resulting knowledge is structured, reorganized and assessed, assigning meta-concepts values such as validity and interest. Storage meta-knowledge integrates new knowledge from the particular application domain together with the associated meta-values, as well as the active meta-knowledge facilitating subsequent knowledge search. These operations increase the knowledge base available to a cognitive system. Reverse operations may then be used for memory search, and, later on, to express and use knowledge.


2. Representing a skill as metaknowledge


The goal of this article is to show that skills and competencies need to be described precisely at the meta-knowledge level. We will summarize here the MOT graphical representation language that we will use as a tool to describe skills and competencies in relation to an application domain.

2.1 The MOT representation system


A basic MOT model is composed of six types of knowledge and seven types of links between them. Knowledge is represented by geometric figures that identify its type. We distinguish abstract knowledge (concepts, procedures, principles) from their corresponding sets of facts (examples, traces, statements). Relations between these entities are represented by oriented links with symbol (C, S, P, I/P, R, I and A) representing the type of relation.

  • The instance (I) link relates an abstract knowledge to a group of facts obtained by giving values to all the attributes (variables); its origin is a concept, a procedure or a principle, and its destination are, respectively, examples, traces or statements.

  • The composition link (C) connects a knowledge unit to one of its components or parts. Any object’s attributes may be specified as component of the object.

  • The specialization link (S) connects one abstract knowledge object to a second one that is more general than the first one.

  • The precedence link (P) connects two procedures or principles, where the first must be terminated or evaluated before the second one can begin or be applied.

  • The input-product link (I/P) connects a concept to a procedure, the concept being the input of the procedure, or a procedure to a concept that is the product of the procedure.

  • The regulation link (R) is directed from a principle towards a concept, a procedure or another principle. In the first case, the principle defines the concept by specifying definition or integrity constraints or it establishes a law or relation between two or more concepts. On the other hand, a regulation link, from a principle to a proce­dure or another principle means that the principle exerts external control on the execution of a procedure or the selection of other principles.

  • The application link (A) is used to associate an object defined as meta-knowledge in another domain, to knowledge in an application domain. We will show that this link is central to the representation of competencies and their relation to skills and application knowledge.
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