Intelligent Research on Chinese and English Listening Teaching Based on Deep Learning
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Abstract
The field of education has witnessed a transformative shift in recent years, with the integration of technology playing a pivotal role in enhancing the learning experience. In this context, recommendation systems have emerged as valuable tools for guiding learners through a vast sea of educational resources. However, privacy concerns and the need for personalized recommendations have posed significant challenges. In response, this paper introduces a novel approach, the Semantic Data Fusion Recommender (SDFR), designed to enhance English teaching while preserving user privacy with the use of federated deep learning model. The SDFR federated deep learning for semantic feature extraction from educational content and combines these features with keyword-based content analysis. Furthermore, it incorporates federated deep learning to protect user data and ensure the confidentiality of individual learning journeys. This paper present experimental results that demonstrate the SDFR's ability to provide highly accurate and personalized recommendations, significantly improving the quality of English teaching. Additionally, our approach adheres to strict privacy standards, making it suitable for deployment in educational settings. The SDFR offers a promising framework for adapting recommendation systems to the evolving landscape of online education, catering to the diverse needs of learners while safeguarding their privacy.
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