Assessing L2 writing formality using syntactic complexity indices: A fuzzy evaluation approach

Abstract

Addressing the ambiguity in formality standards, this study introduces a cutting-edge Multi-dimensional Connection Cloud Model (MCCM) that leverages syntactic complexity indices to develop a fuzzy assessment model for formality in L2 writing. Employing Elastic Net Regression (ENR), the results revealed that four large-grained indices (mean length of sentence, mean length of T-unit, complex nominals per T-unit and complex nominals per clause), and one fine-grained index (average number of dependents per direct object) were significant in predicting the level of formality in L2 writing. To evaluate the model’s predictive power, 45 essays were used as a validation set. The MCCM model achieved a prediction accuracy of 91.1 % (41 out of 45 cases) in matching human ratings, with connection degrees effectively capturing classification uncertainty and boundary transitions. This pioneering framework effectively navigates the complexities and variable distributions of indicators, offering a more objective solution compared to conventional expert evaluations and introducing a novel methodological approach to assessing formality in academic writing.