Title: Research on timeliness evaluation model of online teaching based on intelligent learning

Authors: Wenwen Lv; Xiuzhen Ding; Zarina Abdul Salam

Addresses: International Business School, Universiti Teknologi Malaysia, Kuala Lumpur 541000, Malaysia; School of Economics and Management, Chuzhou University, Chuzhou 239000, China ' History and Sociologi School, Anhui Normal University, Wuhu 241000, China ' International Business School, Universiti Teknologi Malaysia, Kuala Lumpur 541000, Malaysia

Abstract: To improve the effect of online teaching, evaluate the timeliness of online teaching, and provide a more effective analysis method for the current teaching system, we proposed an online teaching timeliness evaluation model based on intelligent learning. Empirical analysis is carried out on statistical data, and a timeliness evaluation model for online teaching based on intelligent learning is proposed. In this model, a statistical information analysis model of online teaching timeliness evaluation is constructed. Then, mining method of association rules is used for segmentation fusion and autocorrelation matching detection of teaching timeliness. Finally, using analysis method for statistical characteristic to conduct statistical analysis and robustness test to the timeliness of online teaching. The simulation results show that this method has a high level of confidence in the timeliness evaluation of online teaching, which improves the quantitative analysis ability of online teaching timeliness.

Keywords: intelligent learning; online teaching; timeliness evaluation.

DOI: 10.1504/IJCEELL.2021.114390

International Journal of Continuing Engineering Education and Life-Long Learning, 2021 Vol.31 No.2, pp.263 - 275

Received: 02 Jul 2019
Accepted: 23 Jun 2020
Published online: 20 Apr 2021 *

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