Optimization of Classroom Teaching Strategies for College English Listening and Speaking Based on Random Matrix Theory

Danlu Liao and Ning Cao

Mathematical Problems in Engineering, 2022, vol. 2022, 1-11

Abstract: Online English teaching systems are more and more widely used in higher education teaching. Based on random matrix theory, this study constructs an optimization model of classroom teaching strategies for college English listening and speaking. By comparing and analyzing the universal properties of the research system and the random system matrix, the model can examine the random properties and special nonrandom properties within the system and solve the quantification problem of college English classroom education. First, based on the existing college English listening and speaking classroom teaching system, with the help of J2EE structure in information technology, the existing college English listening and speaking classroom teaching system is optimized, the customer layer, web layer, business logic layer, and student listening and speaking classroom learning effect management layer are designed, and the system is applied to college English listening and speaking classroom teaching. During the simulation process, a random matrix theoretical dynamics graph library and a subject library were written, so that combining the elements of each vector into a new matrix. Experiments show that the ensemble framework based on ranking learning can not only integrate multiple matrix factorization algorithm models to obtain better recommendation accuracy but also can more fully reflect the eigenvalues of each individual. The experimental results show that when the number of sampling points is 80 and the signal-to-noise ratio is −15 dB, the detection probability is close to 80%, which effectively improves the classroom random detection performance of English teaching and the effect of strategy optimization.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8563978

DOI: 10.1155/2022/8563978

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