Construction of "interactive" English Translation Teaching Model Based on Data-Driven Learning

Abstract

With the growth of international trade, there is a greater demand for people with a diverse professional background and a high level of foreign language translation skills. This thesis covers the fundamental concepts and evolution of data-driven learning, well-known data-driven learning systems, common statistical parameters and analytical statistical methods of data-driven learning, and commonly used data-driven learning indexing software and its basic functions. A basic knowledge outline and data-driven learning thinking method are provided. Although English translation is widely used, there are still many flaws in professional training. In response to the current situation in which college English translation teaching is difficult to adapt to new requirements of college English teaching and talent training, it is proposed to combine multimedia technology and corpus, as well as support constructivist learning theory, data-driven learning theory, and autonomous learning theory, to create a "data-driven Learning (DLL) college English translation teaching model,"beginning with the teaching materials, teaching objectives, and a "data-driven Learning (DLL) college English translation teaching model,"starting from the teaching materials, teaching the impact of the model on college students' translation learning. The results of the experiments show that this mode is associated with a significant improvement in students' translation learning abilities. This model is shown to be effective when used in college English translation classes.