Leveraging machine learning techniques for grading the difficulty of English vocabulary learning

Abstract

As globalization accelerates, the significance of English in international communication becomes increasingly prominent, making the effective learning of English vocabulary a pivotal aspect of language acquisition. This study aims to explore a personalized grading method for English vocabulary learning difficulty through machine learning technology, facilitating learners in more efficiently mastering English vocabulary. Initially, the paper analyzes the limitations of traditional methods for grading the difficulty of English vocabulary learning, highlighting the lack of dynamic adaptation to the differences among learners. Subsequently, it introduces a novel approach to predicting the difficulty of learning English vocabulary using machine learning, particularly through the application of transfer learning techniques at the edge. This method adjusts the prediction model based on the learner’s background knowledge and learning history, thus enhancing the accuracy and applicability of predictions. Finally, by predicting the English vocabulary learning difficulty levels of students at different stages (beginner, intermediate, and advanced), this study validates the effectiveness of the proposed method. The results indicate that the machine learning model employing transfer learning demonstrates significant advantages in grading the difficulty of English vocabulary learning, offering more personalized learning guidance to learners of varying levels. The findings of this research not only provide a new perspective for the field of English education but also offer technical support for designing personalized learning paths.