Title: Research on the integration of English online teaching resources based on improved association rule algorithm

Authors: Hao Liu

Addresses: Public Basic Teaching Department, Henan Vocational University of Science and Technology, Zhoukou, 466000, China

Abstract: With a series of achievements of intelligent algorithms in various fields, people began to try to apply intelligent algorithms to information-based teaching and learning, and use relevant data analysis techniques to improve existing teaching models. The study integrates RBF neural networks with association rule algorithms, and then constructs an English web-based teaching prediction model. Using a crawler data collection tool, the English test scores of a university were selected to test the model. The results show that the improved model has shorter running time and can be iterated to a stable state faster. The model was used for the prediction of actual grades, and the average accuracy of the model was obtained as 95.7%. Comparing the relative error values of the prediction model with different influencing factors, we found that the average relative error was only 0.041. The improved model can achieve better results when used for English score prediction.

Keywords: prediction models; neural networks; association rules; relative error values.

DOI: 10.1504/IJCSYSE.2024.137445

International Journal of Computational Systems Engineering, 2024 Vol.8 No.1/2, pp.48 - 55

Received: 05 Aug 2022
Accepted: 14 Jan 2023
Published online: 19 Mar 2024 *

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