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Title:
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AFFORDANCES OF MACHINE-PROCESSABLE COMPETENCY MODELLING |
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Author(s):
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Onjira Sitthisak , Lester Gilbert |
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ISBN:
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978-972-8924-95-9 |
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Editors:
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Kinshuk, Demetrios G Sampson, J. Michael Spector,Pedro Isaías and Dirk Ifenthaler |
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Year:
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2009 |
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Edition:
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Single |
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Keywords:
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Adaptive assessment, competence, ontology, IMS QTI, distractor |
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Type:
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Reflection Paper |
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First Page:
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534 |
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Last Page:
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535 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Existing e-learning competency standards such as the IMS Reusable Definition of Competency or Educational Objective
(IMS RDCEO) specification and the HR-XML standard are not able to accommodate the level of a competency described
separately from its narrative description; the grading scale of a competency; the success threshold of a competency; or the
structure of competency trees or hierarchies. The proposed competency model addresses these problems and reflects all
relevant features of the learners behaviour and their knowledge, skills, and attitudes that affect their learning and
performance. Statements of competency are machine-readable. 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, distractors, and
question sequences, and offers a semantic structure for further processing. |
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