A Relational ForestSoft Set Approach to Modeling and Optimizing Teaching Quality in University English Translation Programs amid Digital Transformation
Keywords:
Relational ForestSoft Set, Teaching Quality, English Translation, Digital Transformation, Soft Set TheoryAbstract
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.
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