A Prediction Method of Students’ Output and Achievement in Higher Vocational English Online Teaching Based on Xueyin Online Platform

By predicting students’ achievements in advance, teachers and administrators can make and improve teaching plans, optimize teaching resources and improve teaching results in advance. Aiming at the problem of insufficient accuracy of traditional prediction methods, this paper studies a prediction method of students’ output scores in online English teaching in higher vocational colleges based on the online platform of Xueyin.Collect and sort out student behavior data on the online platform of Xueyin, and divide learning behavior into two categories, namely, student basic data and online learning behavior data.Implement data cleaning, data transformation and missing value filling for student behavior data.The attention mechanism is introduced into LSTM to build an Att-LSTM prediction model. The attention mechanism helps LSTM quickly filter out important information from a large number of feature data, focus LSTM on the information that is most helpful for completing the current task, and improve the prediction accuracy of the model by filtering out unimportant data.The results show that the average absolute error and root mean square error are smaller and the coefficient of determination is larger under the application of the research method, which shows that the research prediction method has good effect and higher accuracy in predicting student performance.