Automated Framework For Personalized Learning From Heterogeneous Data Repositories
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Jun 9, 2016
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Dec 3, 2014
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Dec 3, 2014
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Abstract
An automated framework for personalized learning from heterogeneous data repositories is presented. The framework leverages learning modules that are extracted by harvesting and annotating material from online and offline sources. The composed library of modules is then used as a basis for creating and delivering a personalized learning plan to a user who is interested in covering specific learning objectives. The framework introduces a new paradigm to the e-learning space by addressing the automatic collection and annotation of learning modules, the direct mapping of modules to learning objectives, and the continuous improvement of the entire framework by utilizing the feedback collected from the user's interaction with the delivered material.
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