Self-assessment is a crucial component of learning. Learners can learn by asking themselves questions and attempting to answer them. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on a sound pedagogical and technological approach. 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. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates a list of all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge. The way in which the system has been designed and evaluated is discussed, along with its educational benefits.

Abstract. The problem of comparing and matching different learners' knowledge arises when assessment systems use a one-dimensional numerical value to represent "knowledge level". Such assessment systems may measure inconsistently because they estimate this level differently and inadequately. The multi-dimensional competency model called COMpetence-Based learner knowledge for personalized Assessment (COMBA) is being developed to represent a learner's knowledge in a multi-dimensional vector space. The heart of this model is to treat knowledge, not as possession, but as a contextualized space of capability either actual or potential. The paper discusses a system for automatically generating questions from the COMBA competency model as a "guide-on-the-side". The system's novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge.

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Health professions education has moved away from process-based curricula to competency-based curricula. Machine readable and processable health care competencies are still embryonic, pending the emergence of appropriate standards. The IMS Reusable Definition of Competency or Educational Objective specification and the HR-XML competency standard are introduced, compared, and their problems identified in the implementation of exemplar competencies from the UK Royal College of Nursing. An improved competency model is proposed.

Self-assessment is a crucial component of learning.Creating effective questions is time-consuming, however, because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on question templates, criteria for effective questions, and the instructional content and ability matrix. 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 selfassessment.