Combining Big Models and Multimodal Technology to Optimize Learning Path Recommendations in Personalized English Self-learning

With the rapid development of information technology, personalized learning has become an important trend in modern education, especially in the field of English learning. Traditional English teaching methods usually adopt a "one-size-fits-all" approach and fail to fully consider the individual differences of students. This paper aims to solve the problem of insufficient accuracy of path recommendation in personalized English self-learning. To this end, this paper proposes a method to optimize personalized learning path recommendation by combining big models and multimodal technology. Through the analysis and processing of students' learning data by big models, combined with a variety of learning resources such as vision, hearing, and text provided by multimodal technology, the system can dynamically adjust the learning path and provide personalized learning suggestions according to students' learning interests, ability levels, and progress. Specifically, a big model is used to model students' learning history, feedback, and participation, and multimodal technology is combined to generate recommended content that meets students' needs. The experimental results show that the experimental group is superior to the control group in multiple key indicators, including the improvement of learning interest (the average change in the experimental group is 4.3 points, and the control group is 2.5 points), the improvement of task completion (the average learning progress of the experimental group is 93%, and the control group is 75%), and the significant improvement of vocabulary memory retention rate (the experimental group is 88%, and the control group is 73%). In addition, students showed high scores in terms of satisfaction with the recommendation system, relevance of learning content, and adaptability of recommendation difficulty, indicating that the optimization method effectively improved the accuracy and learning effect of personalized learning path recommendations.