English Teaching Quality Prediction and Evaluation using Radial Basis Function Neural Network

In the process of developing present higher education reform, peoples pay much attention to the research of college English education. The goal to enhance college English education is to improve education quality, learning evaluation to enhance the education quality and training. This research proposed a Radial Basis Function (RBF) neural network for English teaching quality evaluation and prediction. The collected dataset is used here and it is preprocessed by The RPF is adoptable for z-score normalization. The preprocessed data features are selected by Chi-square technique and teaching quality is predicted by RBF neural network. The RPF is adoptable for handling non-linear relationship among input and output of teaching quality due to its ability to handle complex, non-linear relationship and generate predictions based on several input factors. The f1-score, precision, accuracy and recall are used for evaluating RBF performance. The RBF achieves $\mathbf{9 6. 2 5} \%$ f1-score, $96.81 \%$ precision, $97.16 \%$ accuracy, $\mathbf{9 6. 3 8 \%}$ recall when compared to state-of-art methods.