An Integrated Approach for Automatic Aggregation of Learning Knowledge Objects
Amal Zouaq, University of Montreal, Canada ; Roger Nkambou, University of Quebec at Montreal, Canada ; Claude Frasson, University of Montreal, Canada
IJELLO Volume 3, Number 1, ISSN 1552-2237 Publisher: Informing Science Institute
This paper presents the Knowledge Puzzle, an ontology-based platform designed to facilitate domain knowledge acquisition from textual documents for knowledge-based systems. First, the Knowledge Puzzle Platform performs an automatic generation of a domain ontology from documents’ content through natural language processing and machine learning technologies. Second, it employs a new content model, the Knowledge Puzzle Content Model, which aims to model learning material from annotated content. Annotations are performed semi-automatically based on IBM’s Unstructured Information Management Architecture and are stored in an Organizational memory (OM) as knowledge fragments. The organizational memory is used as a knowledge base for a training environment (an Intelligent Tutoring System or an e-Learning environment). The main objective of these annotations is to enable the automatic aggregation of Learning Knowledge Objects (LKOs) guided by instructional strategies, which are provided through SWRL rules. Finally, a methodology is proposed to generate SCORM-compliant learning objects from these LKOs.
Zouaq, A., Nkambou, R. & Frasson, C. (2007). An Integrated Approach for Automatic Aggregation of Learning Knowledge Objects. Interdisciplinary Journal of E-Learning and Learning Objects, 3(1), 135-162. Informing Science Institute.
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