Evaluation Method of English-Speaking Self-Learning System Based on Natural Language Processing Technology
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Abstract
In the education, the use of advanced natural language processing techniques has gained prominence for their potential to revolutionize the oral learning experience. Self-learning in English oral learning has become increasingly popular due to its flexibility and accessibility. With the advent of digital resources and language learning apps, students can now engage in language acquisition on their own terms. This paper presented a novel framework that combines the power of the Bidirectional Encoder Representations from the Transformers (BERT) model with the Hidden Condition Random Model (HCRM) for the enhancement of English oral learning. The primary goal is to provide educators and institutions with a robust tool for evaluating the relevance and quality of oral learning materials. The HCRM architecture incorporates sentiment analysis, feature extraction, and classification, making it a comprehensive solution for assessing the suitability of documents in the context of English oral learning. The model takes into account the opinions of both students and teachers, ensuring a holistic perspective on the oral learning materials' effectiveness. By effectively analyzing sentiments and extracting pertinent features, the HCRM facilitates a nuanced understanding of the potential impact of educational content. This paper's findings suggest that the integration of BERT with HCRM has the potential to greatly enhance English oral learning by providing a more accurate, holistic, and data-driven approach to material assessment. The innovative framework presented in this research holds promise for improving the quality and relevance of oral learning materials in the field of English instruction.
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