An online English teaching quality assessment method based on PCA-SVM

To reduce the noise in online teaching quality evaluation, improve the correlation of indicator selection, and obtain scientific online teaching quality evaluation results, a PCA-SVM-based English online teaching quality evaluation method is proposed. This method first selects evaluation indicators and combines them with the PCA algorithm to extract the main components of the evaluation indicators. Then, the SVM algorithm is introduced to obtain its optimal classification hyperplane, to determine the category of the evaluation index. Finally, hyperplane segmentation is performed on the samples of evaluation indicators to obtain the classification results of main component indicator data. Based on the classification results, an English online teaching quality classification evaluation model is designed. The test results show that the correlation coefficient of the proposed method's indicators can reach 0.99, and its multiple evaluation noise is only 0.55 dB, which is superior to the comparison method and has certain feasibility.