Title: Integrating MOOC online and offline English teaching resources based on convolutional neural network

Authors: Kelu Wang; Dexu Bi

Addresses: School of Foreign Languages, Leshan Normal University, Leshan, 614000, China ' College of Educational Science, Guangxi University for Nationalities, Nanning, 530006, China; Department of Elementary Education, Guangxi Police College, Nanning, 530028, China

Abstract: In order to shorten the integration and sharing time of English teaching resources, a MOOC English online and offline mixed teaching resource integration model based on convolutional neural networks is proposed. The intelligent integration model of MOOC English online and offline hybrid teaching resources based on convolutional neural network is constructed. The intelligent integration unit of teaching resources uses the Arduino device recognition program based on convolutional neural network to complete the classification of hybrid teaching resources. Based on the classification results, an English online and offline mixed teaching resource library for Arduino device MOOC is constructed, to achieve intelligent integration of teaching resources. The experimental results show that when the regularisation coefficient is 0.00002, the convolutional neural network model has the best training effect and the fastest convergence speed. And the resource integration time of the method in this article should not exceed 2 s at most.

Keywords: MOOC English; online and offline; mixed teaching resources; intelligent integration method; convolutional neural network.

DOI: 10.1504/IJBIDM.2024.140884

International Journal of Business Intelligence and Data Mining, 2024 Vol.25 No.3/4, pp.271 - 291

Received: 17 Jul 2023
Accepted: 16 Nov 2023
Published online: 03 Sep 2024 *

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