Research on College English Teaching Model Based on Decision Trees

Abstract

English teaching has always attracted much attention. However, the processes of its transmission and acquirement is often divided into two separate parts, which seriously hinders the effective implementation of its objectives. Teachers attach particular importance to the choice of the curriculum structure and teaching material. Students are busy comprehending the assignments their teachers deem important. Under such a scenario, the effective acquisition of knowledge and the development of sustainable comprehensive abilities are ignored. The random forest algorithm in machine learning applications could play important role improving on the current English teaching system. A random forest model is constructed using a decision trees selection method, which focuses on 19 attributes of the English teaching model. Results show, to begin with, that the indigenous teaching plan and environment fail to adapt to the pace of knowledge iteration in the era of big data. Moreover, interactions between teachers and students appear to be shallow with little constructive interaction, causing a decline in the relationship between teachers and students. Last, there is still no signs of any legitimate construction in terms of in-person English teaching, relativity attribute, corpus and platform. Therefore, this paper has proposed a new English teaching model to adapt to the current college English teaching environment. The experimental results show that the method is effective and feasible for the current college English teaching.