Abstract
The aim of this chapter is to illustrate some affordances of machine-processable competency statements. Such competency statements are supported by ontologies and taxonomies of competency. Machine processing can offer interoperable and reusable resources and applications that are pedagogically effective for e-learning and assessment. A competency statement which can be read, processed, and interpreted by machine contributes to the automatic generation of questions and offers a semantic structure using the Web Ontology Language (OWL) to express competencies for further processing. The generated questions are expressed in the IMS Question and Test Interoperability specification (IMS QTI) to enable interoperability.
TopThe Development Of Competency Models
A competency may be considered to be based on subject matter knowledge and skill, contextualized with respect to particular situations or scenarios (Harzallah, Berio and Vernadat, 2006). Competencies may be assembled and linked in a rich data structures. A competency may appear in more than one place in a competencies hierarchy. Thus, it makes sense to capture the data model of competencies in some reusable form, so they have to be defined only once.
The possible requirements for describing competencies based on an analysis of the general structure of existing competency standards and competency ontologies (Trichet and Leclère, 2003; Draganidis and Mentzas, 2006; Schmidt and Kunzmann, 2006) are listed below. The list is general and captures the type of information modelled in existing standards, rather than defining a canonical set of properties.
Key Terms in this Chapter
The Competency Evidence: Substantiates the existence, sufficiency, or level of the competency, and might include test results, reports, evaluation, certificates, or licenses.
Proficiency Level: Indicates the level of proficiency that learners should or do possess of a particular competency.
Capability: Behaviour that can be observed, based on a domain taxonomy of learning such as Bloom’s (Bloom and Krathwohl, 1956), Gagné’s Nine Areas of Skill (Gagne, 1970), or Merrill’s Cognitive Domain (Merrill, 1999).
Attitude: The way in which a learner exhibits their knowledge and skill, perhaps categorised using a version of Krathwohl’s taxonomy (Krathwohl and Anderson, 2002).
A competency: Involves a capability associated with subject matter content, a proficiency level, evidence, any required tools, and definition of the situation which contextualises the competency.
Subject Matter Content: The subject domain of what the learner can do by the end of course.
IMS Question and Test Interoperability (QTI) Specification: A specification to describe a data model for representing question and test data, as well as their corresponding result reports.