Designing an English Teaching Resource’s Information Management System Using Collaborative Recommendation

Abstract

In the modern world, English education institutions are more concerned about how to recommend the required resources to all types of learners and how to improve the application value of the resources. Therefore, this study introduces and combines the collaborative recommendation algorithm to recommend resources to the learners. The development and design of an English teaching resource information management system based on collaborative recommendation technology are in the best interests of English learners. It establishes an English teaching resource information management system to recommend appropriate teaching resources for different English learners and provide personalized recommendations. English teaching resources are used to improve efficiency and make it easier for English learners to find English teaching resources that meet their specific requirements. Firstly, this study briefly introduces the collaborative algorithm and establishes how the score of similar items or users is calculated by the user-based collaborative filtering algorithm and the item-based collaborative filtering algorithm. Based on similarity, the proposed approach recommends the architectural framework of the English teaching system to other users, describes the functions of each module of the system in detail, and detects the system’s application performance through simulation experiments. The result shows that the learning resources are sufficiently accurate. The design system, in comparison with the general information management system, can reduce the time required for users to obtain English teaching resources.

Hybrid Teaching Model of College English Based on Collaborative Filtering Recommendation Algorithm

“…The hybrid teaching model leverages the strengths of both automated algorithms and human expertise. Machine learning algorithms excel at processing vast amounts of data and identifying patterns, trends, and correlations that may not be immediately apparent to humans [7]. However, these algorithms may struggle with understanding complex user preferences, contextual nuances, and subjective factors that influence decision-making [8].…”