A Taxonomy of Generic Skills

As mentioned in the previous chapter, research in cognitive science, cognitive engineering, and education all support the idea that human skills can be described as generic processes. These processes develop through learning and working situations in various domains where knowledge is processed. In their relationship to knowledge, generic skills are the active part of human competencies. Depending on the viewpoint we use, generic skills are problem-solving methods, or active meta-knowledge working on other knowledge, or learning objectives to be acquired. The generic skills framework that will be presented here has been built in order to provide a clear view of the relation between knowledge in any application domain and the “intellectual actions” that enable a person to process and build knowledge. When someone has many such opportunities to exercise generic skills, they the re-construction of their own private universe of generic linkages and connections is made possible. In this chapter, we will develop an integrated taxonomy of generic skills. It will incorporate previous work in cognitive science, software and cognitive engineering, and pedagogical design, some of which was presented in Chapter 6. It is an integrated taxonomy because it can apply to different manifestations of human activity: cognitive, emotional, social, or motor, representing generic skills in the form of process-type knowledge models constituting an operational library that can be used for projects in instructional engineering.

Modeling for Learning Design Repositories

The deployment processes of a new technology or a methodology like Instructional Engineering is crucial if we want R&D results and products to reach end users with innovative products and services that produce quality and growth. These preoccupations are at the origin of the IDLD project that provides the main thread of this chapter. This project is based previous projects in the same area: R2R (Paquette, Marino, De la Teja, Lundgren-Cayrol, Léonard & Contamines, 2005) and edusource (McGreal, Anderson, Babin, Downes, Friesen, Harrigan, Hatala, M., MacLeod, Mattson, Paquette, Richards, Roberts & Schafer, 2004).

Taxonomies of Problems and Generic Skills

The aim of this chapter is to define what we call “generic skills,” i.e. structured sets of intellectual actions, attitudes, values, and principles that are at the heart of human competencies. We will first examine the various systems that offer different yet convergent views regarding skills. One multi-viewpoint approach to the concept of skill first analyses the taxonomies of generic problems developed in software engineering. Generic problems correspond to human problem-solving skills as described in cognitive science. Another viewpoint is the concept of active meta-knowledge that situates skills in the realm of meta-cognition, i.e. as knowledge acting on other knowledge. A third viewpoint considers research in education that presents skills in the form of taxonomies of learning objectives in relation to cognitive, affective, social, or psychomotor domains.

Ontology-Based Software Component Aggregation

The topic of Component-Based Software Development (CBSD) has become very important in industry and research in the last 10 years (Allen & Frost 1998; Object Management Group, 2003). In e-learning, an increasing number of organizations have recognized the importance of building learning technologies by aggregating existent pedagogical software components. To support training processes, Web portals and digitized resources need to be provided to actors in each process. This can be done by aggregating, in a process workflow, different kinds of resources accessible on the Web: documents, simulations, videos, software tools, as well as persons interacting through communication tools and services. These resources are all represented by digital components that need to be aggregated in a proper manner.

Referencing Resources through Ontology Evolution

Evolution is a fundamental requirement for useful ontologies. Knowledge evolves continuously in all fields of knowledge due to the progress in research and applications. Because they are theories of knowledge in a precise domain, Ontologies need to evolve because the domain has changed, the viewpoint of the domain has changed or because problems in the original domain conceptualization have to be resolved or have been resolved (Noy & Klein, 2003). Moreover, in open and dynamic environments such as the Semantic Web, the ontologies need to evolve because domain knowledge evolves continually (Heflin & Hendler, 2000) or because ontology-oriented software-agents must respond to changes in users’ needs (Stojanovic, Maedche, Stojanovic, & Studer, 2003).

Knowledge, Skills, and Competency

A search on the Internet shows the renewed importance given to competency-based approaches in Education and Administration. Ministries of education, school boards, and teacher training institutes use 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, looked upon as the main asset of an organization in a knowledge management perspective. Governmental agencies or professional associations use competency-based approaches to define conditions to the exercise of a profession and to orient their vocational training programs.

Modeling Multi-Actor Activity Scenarios

In this chapter, we concentrate on a very interesting type of model: multi-actor processes. These processes can be found in all domains of human activity, particularly to describe work processes in organizations or learning scenarios. In both cases, we will use the term “activity scenario”. These models are characterized by a sequencing of activities, accomplished by one or more actors who use and produce a variety of resources. We will first present an instructional engineering method, MISA, which enables designers to produce learning or training scenarios. Then we generalize the notion of activity scenario by considering work processes, making a distinction between product-oriented and actor-oriented processes. After that, we present activity scenario modeling tools and a first synthesis of the scenario modeling process that can be applied both to learning and work processes. Through this, we discover that competency modeling in a central element of all these processes.

Types and Examples of Knowledge Models

With only a few object and link types, it is possible to construct representations of complex knowledge systems such as the taxonomies, theories, processes, or methodologies used to describe various fields of knowledge. In this chapter, we will present a taxonomy of knowledge models and provide examples of each model type. Our goal is to demonstrate the generality of the MOT representation system by modeling a wide variety of situations usually modeled by other representations, or in some cases, not at all. In so doing, we will develop a library of models from which designers can choose to adapt to their own areas of concern.

Modeling for Research and Communication

In this concluding chapter, we will give examples where visual modelling techniques have been used in actual research projects. Visual Modelling is particularly useful to the researcher both to organize and guide the research and to communicate its results. Our Research Centre applied this approach in numerous research projects since its creation in 1992. We will start by showing how visual modelling has helped develop a coherent view of Instructional Engineering that led to the design of the MISA method through a number of years. Then we will present another stream of research that has also been supported by knowledge modelling: the development of assistance systems. Finally, we will conclude the chapter by presenting a method that has been used by several doctoral students, in particular by those responsible of the projects presented in section III of this book. These examples will enable us to propose a generic meta-method for research projects and doctoral work that will serve as a conclusion to this chapter.

Visual Ontology Modeling and the Semantic Web

The exponential growth of information available on the Internet makes it increasingly necessary to introduce intelligent agents to facilitate the processing of information and knowledge. A few years ago, one of the founders of the Web and current director of the W3 Consortium, Tim Berners-Lee, along with his colleagues (Berners- Lee, Hendler, and Lassila, 2001), proposed to provide the Internet with information whose meaning, beyond its syntax, could be interpreted by a program that would search information in ways that are far more intelligent than now. This new generation of the Internet has been termed “the Semantic Web”.