A hybrid online and offline teaching model of professional English based on empirical modal decomposition method empowered by digital technology

With the rapid development of artificial intelligence digital technology, its related techniques have penetrated and been applied to the education industry and field. In this paper, we address the limitations of CEEMD in processing student performance prediction data and combine the hybrid gray wolf optimization algorithm with the minimum gating unit recurrent neural network to establish an English performance prediction model by simulating the leadership, hunting, and ranking mechanisms of the gray wolf population, as a way to complete the optimization of EMD limitations. Then, using the empirical modal decomposition method and other auxiliary methods, we conducted an experimental study on the online and offline “hybrid” teaching model for two classes of English majors in the School of Foreign Languages of Huanghe Science and Technology University in 2021 and passed equal-group pre-test and t-test. The results showed that the significance level ^α of English performance in class B was 0.10, and since the probability value P was less than 0.10, it was considered that there was a significant difference between the two overall means, i.e., there was a significant difference between the overall means of the improvement in the performance of class B and class A. The overall performance of class B students was significantly higher than that of class A. This study combines online and offline teaching modes through digital technology to promote the use of online and offline “hybrid” teaching modes in higher education institutions, which is very important.