Title: Extended query model for MOOC education resource metadata based on big data

Authors: Yu Cao; Shu-Wen Chen

Addresses: Mathematics and Information Technology Institute, Jiangsu Second Normal College, Jiangsu, Nanjing 210000, China ' Mathematics and Information Technology Institute, Jiangsu Second Normal College, Jiangsu, Nanjing 210000, China

Abstract: In traditional education resource metadata extended query methods, data recall is poor. In order to solve this problem, an extended query model for MOOC education resource metadata based on big data information fusion clustering scheduling is proposed. The semantic ontology model is adopted to analyse the storage structure of MOOC education resource metadata and extract binary semantic feature quantity of MOOC education resource metadata to construct a binary semantic decision model for extended query of MOOC education resource metadata; the integrated information processing technology of big data is adopted for extended query and adaptive scheduling of MOOC education resource metadata to improve the ability to retrieve and identify metadata. The simulation results show that the proposed method can provide an average precision ratio of 0.976 in extended query of MOOC education resource metadata and good data recall performance.

Keywords: big data technology; MOOC education resource; metadata; extended query; scheduling.

DOI: 10.1504/IJCEELL.2019.102767

International Journal of Continuing Engineering Education and Life-Long Learning, 2019 Vol.29 No.4, pp.374 - 387

Received: 06 Dec 2018
Accepted: 13 Mar 2019
Published online: 02 Oct 2019 *

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