Open Access Article

Title: A study on the optimisation of university English teaching based on an enhanced decision tree model in the context of big data

Authors: Haiyan Cai

Addresses: School of Tourism, Hospitality and Culinary Arts, Chongqing City Vocational College, Chongqing Yong Chuan, 402160, China

Abstract: The purpose of learning is the most important factor affecting an individual's percentage of making notable progress (PMNP). In the context of big data, this study selected a sample of 1,805 non-English majors in a university to investigate the optimisation of university English teaching based on an enhanced decision tree model. The results showed that among the 1,805 student sample, 352 students made more significant improvements in their test scores compared to the last test, accounting for 19.50% of the total PMNP of the entire sample. The Chi-squared automatic interaction detector (CHAID) decision tree model was used to identify implicit and valuable factors influencing teaching quality based on data on the process, conditions and environment of English language teaching. The results show that through calculation of CHAID decision tree, the resultant data of each node is a reflection of the effect of each factor on PMNP.

Keywords: big data; CHAID decision tree; English language teaching; percentage of making notable progress; PMNP; genetic algorithm.

DOI: 10.1504/IJCSYSE.2025.147788

International Journal of Computational Systems Engineering, 2025 Vol.9 No.12, pp.1 - 11

Received: 11 Apr 2023
Accepted: 11 Jun 2023
Published online: 01 Aug 2025 *

 

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