Analysis of the Path of Improving English Listening and Speaking Ability Based on Big Data Technology

Abstract

The training of college students’ English listening and speaking skills needs to be individualized scientifically and strategically in order to get a qualitative improvement. In this paper, we first analyze the correlation between students’ listening and speaking skill levels by using the improved maximum information coefficient (MIC) with the aid of an online platform. The students’ listening ability is assessed by the plain Bayesian algorithm, trained by the neural network to reduce the error, and the similarity between the feature vector and each pattern is calculated so that the element that obtains the highest value is used as the candidate mapping. Next, the schema mapping of data distribution (SMDD) method is combined to obtain the best matching schema. Finally, the effectiveness of English listening and speaking training with the Smart Teaching Platform was investigated through controlled experiments. The results show that in the post-test speech scores, the experimental group’s increase in speech scores was 2.322, while the control group’s increase was only 0.652. The effectiveness of the English listening and speaking ability training strategy is evident due to the obvious difference. This study proposes rational suggestions for improving college students’ English listening and speaking abilities.

Keywords

• maximum information coefficient
• plain bayes
• candidate mapping
• smdd model
• smart teaching platform
• 97m50