Title: Study on online English learning resource push based on Bayesian inference

Authors: Wenfeng Zhang

Addresses: School of Foreign Languages, Jiujiang University, Jiujiang, Jiangxi, China

Abstract: The current online English learning resource push methods have problems of poor customer satisfaction, low reliability of pushed resources and low recall rate of resource pushes. Therefore, this paper proposes an online English learning resource push method based on Bayesian inference. Firstly, obtain online English learning resource data and classify online learning resources. Then, by mining and analysing learner learning data, clustering algorithms are used to locate and infer the learner's learning style. Finally, based on Bayesian inference, a Naive Bayesian classifier is developed, and a network English online learning resource push model is developed to achieve effective network English online learning resource distribution. Through relevant experiments, it has been confirmed that the customer satisfaction of this method varies from 96.0% to 99.8%, the push reliability varies from 90.5% to 99.8% and the resource push recall rate is 99.9%, which has the characteristic of good push effect.

Keywords: Bayesian inference; online English; learning resource push; classify; clustering algorithm; Naive Bayesian classifier.

DOI: 10.1504/IJCAT.2024.141354

International Journal of Computer Applications in Technology, 2024 Vol.74 No.1/2, pp.90 - 98

Received: 07 Nov 2023
Accepted: 05 Mar 2024
Published online: 09 Sep 2024 *

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