Jinyi Gou

This study explores the use of Big Data technologies to optimize Chinese textbook recommendations and resource allocation, aiming to enhance personalized learning experiences. Traditional textbook selection methods often fail to account for individual learner needs, leading to inefficient resource allocation. By analyzing large volumes of learner data—such as behavior, performance, and preferences—Big Data enables a dynamic, data-driven approach to resource distribution. The framework presented in this study leverages collaborative filtering, content-based filtering, and real-time feedback mechanisms to provide learners with personalized textbook recommendations. This system aims to improve the overall quality of Chinese language education by ensuring that learning materials align with the learner's proficiency, learning style, and evolving needs.

Big Data; Chinese Education; Chinese Textbook; Resource Allocation; Personalized Learning

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