Ontology-driven automatic generation of questions from competency models

Abstract

The paper explores some pedagogical affordances of machine-processable competency models. Self-assessment is a crucial component of learning. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. Using ontologies and Semantic Web technologies certain limitations in automation, integration, and reuse of data across diverse applications can be overcome. This paper presents a system for automatically generating questions from a competency framework. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including the level of cognitive ability and the structure of the subject matter. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. The system generates a list of all the questions that are possible from a given learning outcome. Such learning outcomes were collected from the INFO1013 'IT Modeling' course at the University of Southampton. The way in which the system has been designed and evaluated is discussed, along with its educational benefits. © 2013 Springer-Verlag.