A Novel Approach to Evaluating College English Teaching Quality Through Dependency Factor Analysis, Incorporating Point Feature Probability Classification Dependency Factor (Pfp-Df)

Abstract

A Fuzzy Genetic Algorithm (FGA) can be a valuable approach for Social Network Analysis (SNA), where the complexity of social interactions often involves uncertainty and imprecision. In SNA, the structure and dynamics of social networks are analysed to understand various phenomena such as information diffusion, community detection, and influence propagation. FGA integrates the principles of fuzzy logic with genetic algorithms to navigate the intricate landscape of social networks effectively. This paper introduces a novel approach to evaluating college English teaching quality through Dependency Factor Analysis, incorporating Point Feature Probability Classification Dependency Factor (PFP-DF). The proposed framework aims to provide a comprehensive assessment of teaching quality by analysing dependencies between various factors influencing English instruction in college settings. Through simulated experiments and empirical validations, the effectiveness of the PFP-DF model in evaluating teaching quality is assessed. Results demonstrate significant improvements in accuracy and granularity compared to traditional evaluation methods. The PFP-DF model achieved an average accuracy rate of 85% in identifying key dependencies impacting teaching quality, allowing for targeted interventions and improvements. Additionally, the framework enables a more nuanced understanding of teaching dynamics, facilitating data-driven decision-making and continuous improvement in English instruction. These findings underscore the potential of Dependency Factor Analysis with PFP-DF in enhancing the evaluation of college English teaching quality, leading to more effective and impactful educational outcomes.