A Relational ForestSoft Set Approach to Modeling and Optimizing Teaching Quality in University English Translation Programs amid Digital Transformation

Abstract

This study introduces the Relational ForestSoft Set (RFSS), an advanced 
extension of ForestSoft Set theory, to evaluate teaching quality within university English 
translation programs in the era of digital transformation. RFSS integrates graph-based 
dependency modeling, adaptive attribute clustering, relational scoring, and uncertainty 
analysis to effectively address the limitations of traditional evaluation methods. The 
framework is designed to assess curriculum design, teaching effectiveness, and learning 
outcomes using heterogeneous data  ranging from proficiency scores to digital platform 
usage. Four diverse case studies (urban, regional, international, and mixed-profile 
universities) are presented to validate the framework’s robustness and adaptability. 
Compared to earlier ForestSoft approaches, RFSS demonstrates measurable 
improvements in precision and scalability. The study contributes a rigorous mathematical 
formulation, actionable recommendations, and a practical toolset for modernizing 
educational assessment in digitally evolving environments.

DOI: 10.5281/zenodo.15467963