Research On Classification And Classification Of Educational Resources Based On Semantic Grid And Clustering Method

In the last decade, education informationization boosts. A large amount of educational resources are created and made publicly available. To effectively use numerous educational resources, it is important to manage them appropriately. However, there remain many challenges. For instance, educational resources are dispersal stored. Redundant systems are developed to provide educational resources, producing multi-source heterogeneous educational resources. To this end, previous studies apply semantic grid technique for better management of educational resources. Specifically, they extract topics from educational resources, compute semantic similarity between the topics, and utilize machine learning techniques to cluster the topics. The clustered topics help classify similar educational resources together, and therefore improve the integration of educational resources. Based on the analysis of the prior findings, this paper study the meta-data standards of educational resources firstly and then discusses the semantic grid for unstructured data, this paper eventually proposes a clustering algorithm based on semantic grid, and the algorithm was applied to generated the class label of the educational resources.In addition to existing studies, this work proposes to integrate metadata analysis, semantic grid technique, resource description framework(RDF). Semantic grid technique is applied to extract and select features to classify unstructured data of educational resources. As a summary, the major contributions are as follows:1) Many widely used metadata standards are analyzed and compared. Sharable content object reference model(SCORM) is then chosen to describe educational resources. A universal framework to manage educational resources is proposed by integrating semantic grid technique, RDF, and SCORM.2) This work reviews several clustering techniques(e.g., semantic grid model and metadata-based semantic grid). When processing unstructured data, semantic grid based clustering algorithm is proposed to address the problem of selecting features by clustering-based method.3) In the experiments, a number of online educational resources are crawled and processed. Furthermore, this work implements semantic grid based algorithms to extract and select features for educational resources.