A Resource Recommendation Algorithm for Online English Learning Systems Based on Learning Ability Evaluation

Abstract

Mature online English learning platforms should provide students with necessary learning resources, ensure efficient access to learning projects, and offer the optimal learning experience. However, the traditional recommendation methods for English learning resources cannot satisfy the in-depth learning demand of students. To solve the problem, this paper designs a resource recommendation algorithm for online English learning systems based on learning ability evaluation. Firstly, the workflow of the designed algorithm was introduced, and a four-layer test system was developed for students’ English learning ability evaluation. Next, an English learning ability evaluation method was proposed based on the maximum expectation algorithm, as well as the estimation methods for parameters like learning ability, degree of discrimination, difficulty, and guess coefficient. Experimental results demonstrated the good effect of the proposed resource recommendation algorithm. The research findings provide a reference for resource recommendation of other online learning systems.