Abstract

To discover knowledge form the available volumes of learning objects, the tasks to manage, analyze, search, filter, and summarize them should be automated. This requires understanding of the objects' contents. The main objective of our work is to advance the state-of-the-art techniques in learning objects mining by developing and demonstrating the use of semantic understanding as basis of its mechanisms. The approach is based on semantic notions to represent text, and to estimate distances between the represented text contents of the objects. The representation reflects existing relations between concepts and facilitates accurate similarity judgments that results in better mining performance. Mining processes are carried out by the developed models and algorithms. In this paper, the application of the semantic understanding-based approach in clustering learning objects is presented. Experimental work is reported, and its results are presented and analyzed 1.

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