Research pick: Speak easy - "A study on the optimisation of university English teaching based on an enhanced decision tree model in the context of big data"

Research conducted at a Chinese university and reported in the International Journal of Computational Systems Engineering has looked at how machine learning and big data techniques might be used to identify the most influential factors in English language learning for non-English majors. The researchers analysed the academic progress of 1,805 students using a refined machine learning model and found that student motivation was the single most important driver of improvement, outweighing even the teaching methods or mode of instruction.

The findings emerged from an analysis using an advanced decision tree algorithm, an enhanced CHAID (Chi-squared Automatic Interaction Detector) decision tree coupled with a genetic algorithm that filters out irrelevant data and has improved predictive accuracy.

A decision tree is a machine learning model that maps the relationships between variables in a branching format. The CHAID variant is particularly suited to education research, as it handles categorical variables well, including learning environments and teaching styles. By enhancing the CHAID algorithm with genetic programming, the researchers were able to evolve the decision model iteratively, improving its ability to identify key patterns in the student data.

The primary metric analysed was the percentage of students making notable progress. Just under 20 percent of students in the sample, 352 individuals, met this threshold. The model was then tasked with identifying what differentiated these students from the majority.

The answer, the researchers found, lay first and foremost in student motivation. Whether driven by career ambitions, academic goals, or personal interest, a student’s reason for studying English had the strongest correlation with measurable improvement. Teaching methods, ranging from interactive approaches to more traditional lecture formats, ranked second in influence, followed by the mode of instruction, whether face-to-face or online learning.

English continues to serve as a bridge language in academia, commerce, and international dialogue, so effective English instruction is a priority in educational institutions around the world. The study provides a new insight into how teaching might be improved, specifically in China, but perhaps elsewhere too.

Cai, H. (2025) ‘A study on the optimisation of university English teaching based on an enhanced decision tree model in the context of big data’, Int. J. Computational Systems Engineering, Vol. 9, No. 12, pp.1–11.