Optimization of English Learning Platform Based on a Collaborative Filtering Algorithm

Abstract

This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific techniques of collaborative filtering. After introducing the recommendation system and collaborative filtering algorithm, this paper elaborates on the theoretical basis and technical principles of the recommendation algorithm based on cognitive ability and difficulty and provides an in-depth analysis of the design and implementation of the recommendation algorithm by combining cognitive diagnosis theory, readability formula, and English knowledge map, which provides a comprehensive and solid theoretical guidance and support for the application development of the online English learning platform. The system is tested by building a Spring Cloud platform, importing actual business data, focusing on the validation of the recommendation model, and connecting the recommendation system to the formal production system to analyse the recommendation effect. Compared with the original recommendation method, the online English learning platform designed and implemented in this paper based on the cognitive ability and difficulty collaborative filtering recommendation algorithm has a better recommendation effect. The system is proved to be well designed and has certain reference and guiding value for the whole web-based online learning platform and has a broader application prospect nowadays and in the future.

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Agent-Based Personalized Assessment Tasks Recommendation Considering Objective and Subjective Factors

“…Wei et al control the difficulty of assessment tasks matching learning ability to motivate learners to achieve higher achievement [9]. Tang applies collaborative filtering to select learning materials matching learning ability [10]. However, the above papers do not dynamically adjust recommended learning materials considering changing learning ability.…”

“…In (10), acc d refers to the difficulty accuracy; n l refers to the number of learners; d r, i refers to the recommended difficulty of assessment tasks for learner i; d a, i refers to the difficulty of the generated assessment tasks for learner i; n l refers to the number of the learners.…”