Research on data-driven adaptive wisdom teaching of business English combined with machine learning algorithms

Abstract

In the research of data-driven adaptive smart teaching in business English, existing algorithms are prone to overfitting when dealing with complex teaching scenarios, resulting in low accuracy in prediction and decision-making and difficulty in effectively generalizing to new contexts. This study integrates machine learning algorithms and data-driven methods to deeply mine massive learning data and construct an efficient adaptive teaching model. The model uses advanced machine learning techniques to identify each learner’s needs and learning paths accurately. Research data shows that the practical application of this model has increased the learning efficiency of business English learners by 30% and significantly improved their learning satisfaction. In terms of a data-driven approach, over 5 million pieces of learning data were collected and analyzed, covering learners’ learning habits, performance changes, feedback, and other aspects. These data provide a strong basis for optimizing teaching strategies, making teaching more accurate and personalized. This study utilizes machine learning algorithms to achieve immediate responses and personalized guidance for learners’ questions. This system improves learners’ learning efficiency and greatly enhances their learning experience. Not only does it provide strong technical support for business English teaching, but it also offers new ideas and directions for future research on intelligent teaching.