Abstract
The aims are to ameliorate the dull classroom atmosphere and unsatisfactory teacher-student interaction and cultivate students’ reading, writing, and translation (R-W-T) English proficiency level (EPL). Specifically, this paper designs an optimized polynomial kernel-based support vector machine (SVM) classification algorithm based on the machine learning (ML) theory. Firstly, this paper expounds on the correlation between ML and teaching classrooms to analyze the optimization direction in the application of ML in classroom teaching. Afterward, SVM and RF algorithms are selected for data normalization optimization, and their optimal hyperparameters are analyzed. Consequently, an experiment is designed to compare the classification accuracy of the algorithms before and after optimization, and the polynomial kernel-based SVM algorithm is proved to present the most remarkable improvement and accuracy after optimization, which is as high as 95.23% or 66.96% improvement. Therefore, the polynomial kernel-based SVM algorithm is chosen for the college English (R-W-T) classroom-oriented human pose recognition (HPR) system. Thus, English teachers can better grasp the students’ psychological state and classroom atmosphere and ameliorate unsatisfactory teacher-student interaction in students’ English (R-W-T). The proposal plays a positive role in cultivating college students’ strong (R-W-T) EPL, which is of great significance in improving the English classroom.
Highlights
At present, English remains the most widely spoken global language
This paper expounds on the correlation between machine learning (ML) and teaching classrooms to explore the optimization scheme for ML-based classroom teaching
The accuracy of polynomial kernel-based support vector machine (SVM) has reached 95.23%, whereas that of other algorithms is less than 50%
Summary
English remains the most widely spoken global language. there is an increasing demand for college students to perfect their reading, writing, and translation (R-W-T) English proficiency level (EPL) through college English (R-W-T) classroom [1]. Most existing college English (R-W-T) classrooms are characterized by a single teaching mode, a dull classroom atmosphere, and insufficient classroom interaction. This is due to the traditional one-to-many teaching approach, in which teachers have limited control over individual students’ learning situations [2]. With the development of artificial intelligence (AI) and machine learning (ML), computers are endowed with powerful analytical learning abilities, providing solutions for students’ classroom behavior analysis and classroom teaching optimization. Some scholars analyze the students’ in-classroom attention direction by analyzing their head pose features through random forest (RF) and iterative regression algorithm. The application of existing AI technology in classroom behavior analysis
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