Title: Analysis of optimisation method for online education data mining based on big data assessment technology

Authors: Guixian Su

Addresses: School of Economics and Management, Xiamen University of Technology, No. 600 Ligong Road, Jimei District, Xiamen, 361024, Fujian Province, China

Abstract: The existing online education data mining method has the problems of low coverage, low accuracy and high error rate. An optimisation method for online education data mining based on big data assessment technology is proposed. Combining with big data assessment technology, the number of online education is determined according to the target to be evaluated. According to the collected content, the abnormal data, missing data and noisy data of online education are preprocessed. According to the preprocessed results, a model of online education information flow is constructed, and the spectrum characteristics of discrete samples of educational data are extracted. PSO algorithm is used to cluster and optimise the features of online education data to realise the optimisation of online education data mining. The experimental results show that the proposed method can improve the mining accuracy, reduce the mining error rate and ensure the stability of data mining.

Keywords: big data assessment; education data; accuracy; coverage; mining optimisation.

DOI: 10.1504/IJCEELL.2019.102768

International Journal of Continuing Engineering Education and Life-Long Learning, 2019 Vol.29 No.4, pp.321 - 335

Received: 04 Dec 2018
Accepted: 31 Jan 2019
Published online: 02 Oct 2019 *

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