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<p>The study explores the development of an individualized Chinese teaching path based on fuzzy reasoning. By analyzing data from 500 junior high school students in Beijing, we integrated learning interest, academic performance, classroom engagement, family background, and linguistic characteristics using text mining technology. The results demonstrate that applying fuzzy inference and real-time data processing significantly enhances students’ reading comprehension, writing proficiency, and classroom participation. The optimized teaching strategy dynamically adapts to students’ learning behaviors, ensuring a personalized and effective learning experience. Future applications of this research include integrating AI-driven adaptive learning technologies, expanding to other subjects, and developing an intelligent educational platform for real-time learning analysis. To address the novelty concern beyond static fuzzy classification, we introduce a Streaming Neuro-Fuzzy Rule Ensemble (SNRFE) that couples minute-level stream processing with meta-learning of membership functions and a contextual bandit for rule-set selection. This hybrid, online framework departs from fixed rule bases by learning student-specific controllers on the fly, yielding adaptive, scalable path planning that is theoretically grounded and practically deployable.</p>

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Individualized Chinese teaching path planning based on fuzzy reasoning

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Tingting Rao

摘要

The study explores the development of an individualized Chinese teaching path based on fuzzy reasoning. By analyzing data from 500 junior high school students in Beijing, we integrated learning interest, academic performance, classroom engagement, family background, and linguistic characteristics using text mining technology. The results demonstrate that applying fuzzy inference and real-time data processing significantly enhances students’ reading comprehension, writing proficiency, and classroom participation. The optimized teaching strategy dynamically adapts to students’ learning behaviors, ensuring a personalized and effective learning experience. Future applications of this research include integrating AI-driven adaptive learning technologies, expanding to other subjects, and developing an intelligent educational platform for real-time learning analysis. To address the novelty concern beyond static fuzzy classification, we introduce a Streaming Neuro-Fuzzy Rule Ensemble (SNRFE) that couples minute-level stream processing with meta-learning of membership functions and a contextual bandit for rule-set selection. This hybrid, online framework departs from fixed rule bases by learning student-specific controllers on the fly, yielding adaptive, scalable path planning that is theoretically grounded and practically deployable.