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.
Highlights
As the information technology is developing at an astonishing pace, students' learning habits and behavior patterns have undergone major changes along with the wide application of handheld smart mobile terminals such as mobile phones and PDAs
Different from traditional algorithms, this paper proposes a resource recommendation algorithm for English online learning systems based on student learning ability evaluation
At first, the performance fluctuation is analyzed and the trend is predicted based on students’ English learning ability evaluation results and their online-learning process test results; combining with the evaluation results of students’ English learning ability, it clusters the students based on their ability level; at last, according to the analysis on the student groups’ cognition level, knowledge acceptance level, and individual learning preferences, it achieves the planning of students' online English learning paths and the recommendation of personalized learning resources
Summary
As the information technology is developing at an astonishing pace, students' learning habits and behavior patterns have undergone major changes along with the wide application of handheld smart mobile terminals such as mobile phones and PDAs. In this mechanism, at first, the performance fluctuation is analyzed and the trend is predicted based on students’ English learning ability evaluation results and their online-learning process test results; combining with the evaluation results of students’ English learning ability, it clusters the students based on their ability level; at last, according to the analysis on the student groups’ cognition level, knowledge acceptance level, and individual learning preferences, it achieves the planning of students' online English learning paths and the recommendation of personalized learning resources. Step 5: To avoid repeated recommendation resources, the similarity of learning resources is measured based on learning projects; according to the measurement results, the resources are screened before recommendation
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