Abstract
The delivery of trainings to diversified and constantly changing audiences is expensive and time consuming. We propose a computational approach addressing this issue by providing an adaptive training delivery framework. The approach relies on case-based reasoning (CBR) as a problem-solving method whereby cases are used rather than a prohibitive number of rules to store knowledge, i.e., experience. CBR is indeed accepted as one of the mainstream paradigms in artificial intelligence since it represents both knowledge and reasons about it. This choice is further motivated by the fact that the process of adaptation to different audiences is built on the traces left by previous learning tasks and practices that can be stored and automatically retrieved. Moreover, to address the crucial and pending issue of case indexing in CBR, we use ontologies to model and index the learning objects that represent the trainings core, thus reducing the retrieval process and improving search. Substantially, we develop an adaptation algorithm responsible for the required corrective actions in the adaptive delivery of trainings destined to diversified and heterogeneous learners.
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Mansouri, D., Mille, A. & Hamdi-Cherif, A. Adaptive Delivery of Trainings Using Ontologies and Case-Based Reasoning. Arab J Sci Eng 39, 1849–1861 (2014). https://doi.org/10.1007/s13369-013-0761-4
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DOI: https://doi.org/10.1007/s13369-013-0761-4