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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Sitthisak, O., Gilbert, L., Davis, H.C. (2009). Transforming a Competency Model to Parameterised Questions in Assessment. In: Cordeiro, J., Hammoudi, S., Filipe, J. (eds) Web Information Systems and Technologies. WEBIST 2008. Lecture Notes in Business Information Processing, vol 18. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01344-7_29
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