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The Predictive Power of Linguistic Features on EFL Writing Quality Among Chinese Secondary Students: A Multilayer Perceptron Approach
Gavin Bui
Asian Journal of English Language Teaching ›› 2026, Vol. 35 ›› Issue (3) : 40-56.
PDF(440 KB)
PDF(440 KB)
The Predictive Power of Linguistic Features on EFL Writing Quality Among Chinese Secondary Students: A Multilayer Perceptron Approach
As one of the pioneering applications of machine learning for quantitative data analysis in applied linguistics, this study investigates the predictive power of linguistic features on English as a Foreign Language (EFL) writing quality. To overcome the limitations of traditional linear regression, specifically multicollinearity and non-normal data distributions, we employed a Multilayer Perceptron (MLP) neural network to analyse 96 essays from Chinese secondary school students. The model evaluated the relative predictive importance of various lexical, syntactic, fluency, and accuracy features. Demonstrating exceptional fit, the neural network accounted for approximately 80% of the variance in overall writing scores with no evidence of overfitting. Sensitivity analyses revealed that lexical diversity was the most robust predictor of writing quality (100% normalised importance), followed by grammatical accuracy (60.4%). Conversely, syntactic complexity, text length, and lexical sophistication exhibited comparatively minimal influence. These findings underscore the critical role of vocabulary diversity in evaluating adolescent EFL writing, while successfully establishing neural networks as a powerful, innovative methodological tool for future applied linguistic research.
EFL writing / multilayer perceptron / linguistic predictors / lexical diversity / machine learning as statistical modelling
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