A holistic evaluation framework for Chinese graded reading system
Rende Li, Jian Zhang · Journal of Data and Information Science · 2025
ABSTRACT Purpose This study aims to address the lack of a unified grading framework in Chinese reading by proposing a comprehensive model integrating linguistic complexity and reading experience, to evaluate the difficulty level of Chinese books. Design/methodology/approach We utilized the Probabilistic Fuzzy Linguistic Technique (PFLT) for subjective indicators related to reading experience. Combined with collected objective indicators data, the Entropy Weight Method (EWM) is used for objective weight allocation, and Tomada De Decisão Interativa Multicritério (TODIM) is applied for multi-criteria decision-making. Findings The results demonstrate that the proposed framework significantly enhances the alignment between text complexity and reader engagement, achieving a high consistency with authoritative benchmarks. Research limitations The study may be limited by the scope of subjective and objective indicators used; further validation with more diverse data sources could strengthen generalizability. Practical implications This approach shows promise in advancing the standardization and objectivity of Chinese reading grading systems, with potential applications in educational and publishing contexts. Originality/value This study offers a structured evaluation model that incorporates both linguistic complexity and experiential factors, providing a novel and comprehensive method for assessing Chinese text difficulty.