Grouping Algorithm for Learning Community of Interest Based on Ontology-based VSM

Abstract:

optimized division is a research of great importance in computer-supported collaborative learning (CSCL).The key to establishing a learning community of interest is to determine and calculate the interest similarity between the learners. To get rid of the disadvantages of neglecting the semantic relevance between terms in the traditional vector space model, ontology-based Vector Space Model(VSM) using semantic web technology is presented to calculate the learner’s interest eigenvector, which can enhance the relative accuracy of the interest similarity. And a self-organization grouping algorithm for community is put forward, based on the learners’ interest similarity match-degree and its concentration. Great dimensions would take place with the ontology to construct vector space, thus Concept Indexing(CI) method and reasonable treatment to matrix of interest Eigen value are here used to promote the calculation efficiency. Finally, an experimental analysis of online education cases is carried out to verify the model algorithm with high efficiency and good scalability.