An English teaching quality evaluation model based on Gaussian process machine learning

Abstract

Background The efficiency of conventional English teaching quality evaluation is comparatively small, and evaluation statistics are challenging. To investigate the use of artificial intelligence (AI) technology in teacher teaching assessment, a machine learning algorithm is proposed to create a teaching evaluation model suitable for the current educational model to assist colleges and universities in overcoming existing teaching challenges. Objectives The proposed Machine learning‐based Gaussian process model (MLGPM) improves the student's language skills. The proposed model uses Gaussian mixed model to express the circulation features of samples and enhances the support vector machine. Therefore, this paper suggests an active learning algorithm that, in association with Gaussian mixed model and sparse Bayesian learning, strategically chooses and labels samples to construct a classifier that syndicates the distribution characteristics of the samples. As a result, the accuracy of a considered quality index for English classrooms is verified, and the quality and control of English as a foreign language can be enhanced. Results The experiment results show that the model presented in this study is effective and beneficial when assessing the efficiency of teaching in universities and analyzing big data sets. Conclusion The simulation analysis with student performance improvement in English teaching quality using machine learning high fluency rate of 95.3, high accuracy ratio of 98.1%, improve vocabulary prediction ratio of 94.6%, improve passage prediction ratio of 92.7%, enhance learning rate of 95.2%, reduce the error rate of 24.1%, F1‐score of 91.5% and assessment score of 92.1% when compared with other methods.

A Classification Technique for English Teaching Resources and Merging Using Swarm Intelligence Algorithm

“…Note that the good new particles are always replaced by the poor ones, and enter the next iteration. Figure 2 presents the flow chart for the proposed GEM-PSO algorithm [39]. is should be noted that the seven steps, as shown in Algorithm 2, is a rundown of the primary phases involved in enhancing the traditional particle swarm algorithm.…”

Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization

“…The method presented in Ref. [ 14 ] also uses a similar strategy. In this method, which is called Machine learning-based Gaussian process model (MLGPM), the goal is to improve students' language learning skills by using ML techniques based on the Gaussian process model.…”

Quality Improvement Model of English Teaching in Universities Based on Big Data Mining

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