A Variable Precision Rough Set Model for Knowledge-assisted Management in Distance Education

Abstract

To enable the teaching administrator to better obtain effective knowledge from a large amount of information to assist management and improve the efficiency and level of teaching management, a variable precision rough set model for knowledge assisted management of distance education was proposed. First, based on the theory of complete reduction and knowledge extraction, the proposed pedigree ambiguity tree was used as a strategy for obtaining complete reduction. An algorithm for obtaining a complete set of reductions was given. Then, by studying the process of knowledge extraction, a multi-knowledge extraction framework was put forward. The process of data conversion was completely realized. Finally, experimental verification was performed. The results showed that the proposed model overcame the effect of noise data in real data and improved the efficiency of the algorithm. Therefore, the model has high universality.