Design and Implementation of an English Learning System Based on Intelligent Recommendation

Abstract

The Internet has led to the rapid development of online education, but it has also caused redundancy in educational information. How to choose appropriate courses from a large number of online education resources has become a major problem for current learners. Therefore, the study proposes an English learning system based on efficient and deep Matrix decomposition. The results of the experiments showed that, in practical teaching applications, about 57.5% of students with good grades have improved their grades due to the use of the English learning system proposed by the research institute, with only about 17.5% of their grades decreasing. 67.5% of students with average grades have improved their grades after using the system, with only 10% decreasing. Among the students with poor grades, about 50% of them improved their academic performance through the system, while about 27.5% of them experienced a decrease. Meanwhile, the experiment also tested the efficient deep Matrix decomposition model in the learning system: the minimum absolute average errors of the model on different data sets are about 0.61, 0.69, 0.77 and 0.82, respectively. The minimum Root-mean-square deviation is about 0.91, 0.98, 1.06 and 1.1, which is far lower than other recommended models. The above results show that the system constructed in this paper can recommend courses according
to students ‘actual learning level, and can effectively improve students’ academic performance in the actual teaching process.