Comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction
by Jia Peng; Xikai Li
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 33, No. 2/3, 2023

Abstract: In order to solve the problems of traditional MOOC teaching resources retrieval methods, such as low retrieval accuracy, poor retrieval recall and low retrieval efficiency, this paper proposes a comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction. The feature value of MOOC teaching resources is extracted by grey level co-occurrence matrix, and the similarity calculation of resource content features is realised by feature attribute annotation. The MOOC teaching resources search process is designed, and the comprehensive retrieval of MOOC teaching resources is realised by feature value extraction. The experimental results show that the retrieval accuracy of this method is low, the retrieval recall rate is as high as 95%, and the retrieval efficiency of teaching resources is effectively improved.

Online publication date: Wed, 01-Mar-2023

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