A Python‑Based Computational Pipeline for Syntactic Complexity Progression Analysis in Vocational High School English Textbooks
Year: 2026
Type: conference-paper
Abstract: This paper focuses on the design, optimization and improvement of an automated computational pipeline for syntactic complexity analysis. The pipeline integrates TAASSC tool secondary encapsulation, Python-based data standardization, cross-tool format unification, and Kruskal-Wallis statistical validation, solving the output heterogeneity and low reproducibility problems of traditional linguistic analysis tools. The proposed pipeline integrates three core components: (1) TAASSC, a natural language processing tool for extracting eight syntactic indicators; (2) Python with pandas, NumPy, Matplotlib, and Seaborn for data normalization, coefficient of variation (CV) calculation, and multi‑format visualization; and (3) statistical validation using the Kruskal‑Wallis nonparametric test implemented in jamovi 2.4.13. The pipeline addresses the critical issue of output format heterogeneity between different linguistic tools by unifying all indicators into a per‑100‑word or per‑text scale. Applied to the four‑volume national textbook series English for New Skills (Foreign Language Teaching and Research Press), the proposed method reveals that syntactic complexity does not increase linearly but exhibits a significant jump between Volume 2 and Volume 3. The CV‑based classification identifies MLS, MLT, C_S, and VP_T as high‑disparity indicators, while MLC remains stable across volumes. This automated framework transforms experience‑based textbook evaluation into a reproducible, data‑driven process, offering empirical support for AI‑assisted language teaching systems and large‑scale second language material analysis.
Authors: Xike Wang, Shuai Hu
Institution: Bohai University
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7213229769