Blended Teaching Mode Model of College English Based on Collaborative Filtering Recommendation Algorithm

Abstract

The incorporation of collaborative filtering recommendation algorithms into blended teaching approaches for college English marks a big step forward in language education. This study investigates the theoretical underpinnings, practical ramifications, and pedagogical issues for adding collaborative filtering algorithms into blended learning environments. Collaborative filtering algorithms produce individualized learning resource recommendations based on user interactions and preferences, increasing student engagement and motivation. However, issues like as data privacy, algorithmic biases, and user approval must be addressed to ensure that collaborative filtering is implemented effectively in the classroom. This study explains the potential of collaborative filtering recommendation algorithms to improve language learning results in college English courses by doing a thorough literature review. It also provides ideas for educators and policymakers on how to handle the complexity of incorporating collaborative filtering recommendation algorithms into blended learning models. Finally, this paper adds to the promotion of novel pedagogical approaches and the improvement of language learning experiences among college students. As a result, the enhanced collaborative filtering recommendation system had a slightly greater recall rate and precision than the pre-optimized method.