Problems, skills and competency are important interrelated concepts we need to investigate anew, especially in the context of tele-learning systems’ design. Because they involve generic skills, competency statements can only be described at the metaknowledge level, more precisely as generic processes related to domain-specific knowledge. To describe both levels of knowledge and their interrelation, we have developed a graphical knowledge representation language and a tool, MOT, central to our instructional engineering methodology (MISA). We will present here an integrated taxonomy of skills that draws upon three fields of knowledge: education, software engineering and artificial intelligence. This taxonomy will help us support some of the main instructional design products in MISA: standardized representable competency statements, multi-level knowledge models, process-based learning scenarios and, finally, actors, roles and resources in a telelearning system.
A search on the Internet is sufficient to show the renewed importance given to competency-based learning and training. Ministries of education, school boards and teacher training institutes present competency profiles to define school programs or required qualities from the teachers, especially in the use of technologies in education. Consulting companies present their expertise by enumerating competencies, marketing their services in this way. Other companies offer services or computerized tools to help their prospective customers define or manage the competence of their staff, identified as the main asset of an organization in a knowledge management perspective. Governmental bodies or professional associations use competency-based approaches to define conditions to the exercise of a profession and to orient their vocational training programs.
Problems, skills and competency are important interrelated concepts we need to investigate anew, especially in the context of tele-learning systems’ design. We seek an integrated view of these concepts for competency-based instructional design, especially in the context of tele-learning. We can achieve this integrated view by modeling generic skills and specialized knowledge in different byt interrelated knowledge domains. Theses domains will be labeled respectively as meta-knowledge domain and application domain.
To describe both levels of knowledge and their interrelation, we have developed a graphical knowledge representation language and a tool, MOT, central to our instructional engineering methodology (MISA). We have described in Paquette 2000 the main features of the MISA method. Some aspects of the MOT system have been presented in Paquette 1994, 1999.
Drawing upon three fields of knowledge and corresponding viewpoints, education, software engineering and artificial intelligence, we will present here an integrated taxonomy of skills that is instrumental for instructional design. This taxonomy will serve to support some of the main instructional design products in our instructional design methodology: standardized competency statements, multi-level knowledge models, process-based learning scenarios and, finally, actors, roles and resources at delivery time in a telelearning system.
1. Problems, skills and metaknowledge
Competencies can be defined by associating an actor with the general skills he/she can apply to an application knowledge model to solve a corresponding class of problems. To solve classification, diagnosis or construction problems for example, it is necessary to achieve some corresponding task and, for this, to mobilize corresponding classification, diagnosis or construction skills. A competency statement linking skills to knowledge in an application domain is defining, at the same time, learning and teaching goals as well as intellectual processes for the resolution of a class of problems related to the skills. In this section, we will define skills as active metaknowledge enabling us to represent them in a knowledge model linked to an application model. Both skills and application knowledge, and their relationships, thus become objects to be learned.
From the literature on competency profiles, we have hypothesized the following principles that help situate this concept into the theoryofaction framework.1 .
The persons whose competencies are described are not simple operators or factors to be evaluated; they are actors endowed with intentions, situated in a cognitive and social context.
The heart of a competency lies in the association between skills, seen as generic cognitive processes, and specific knowledge; we seek here to avoid the atomization of competence into the traditional categories of knowledge, skills and attitudes2.
Competencies are components of a person’s mental model resulting from active meta-knowledge enacted on specialized knowledge in a application domain, allowing to act on the later in various ways; this approach integrates the cognitive and meta-cognitive aspects that must be both present for thoughtful human action and competencies.
Competencies also describe outcomes qualified by the level of excellence of the observed performance and confirmed by social reward, as in current models for educational objectives.
Competency definitions can thus be used for the assessment of learners as well as developmental objectives.
To concretize operationaly these ideas, we will now considered three domains offering different viewpoints on the notion of skills: educational objectives, software engineering and artificial intelligence.