Self-Study System Evaluation of Oral English Based on Limit Theory

Xing Ming, Xihong Wang, Wenlan Liu and Xiantao Jiang

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

Abstract: With the globalization of the contemporary social economy and its ever-increasing influence, it is particularly important to master a common language in the world at this stage English. For the subjects who have the opportunity to learn English, their listening, reading, writing, and other abilities are relatively good, but the speaking ability is relatively lacking. Therefore, the self-study of spoken English is particularly important for the improvement of the practical application ability of English, and an efficient self-study system will also play a better role in the self-study process. The efficiency of a system depends on the maximum and minimum magnitudes that the system can carry. Therefore, for the self-learning system of spoken English in this paper, it is necessary to calculate the maximum and minimum carrying capacity. Therefore, it is necessary to deal with the limit thinking of the maximum and minimum carrying capacity of the system. The evaluation based on limit theory in this paper is a kind of evaluation of the maximum and minimum carrying capacity of the English oral self-learning system by using the convergence and dispersion characteristics of limit thinking. In order to evaluate the performance of the oral English self-learning system, this paper implements the dynamic evaluator of the oral English self-learning system, and constructs a hardware system for the self-learning oral English. This paper also uses the hidden Markov model in the self-learning system and the application of the central limit theory. The corresponding experimental results show that the mean value of the spoken English test data is 25.1337, and the standard deviation is 2.01385, which is in line with the normal distribution. It shows that the evaluation based on the limit theory can make the operation of the spoken English self-learning system stable.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2523088

DOI: 10.1155/2022/2523088

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