Prediction and Evolution of Distance Education Learners’ Feedback Attitudes by a Deep Learning Approach

Abstract

In recent years, the feedback attitude towards teachers has become one of the most important factors affecting learners' trust in teachers' teaching ability and changing learning plans, so the research on learners' feedback attitude has received extensive attention from experts and scholars at home and abroad. Most of the existing relevant research focuses on empirical research and behavioral research using questionnaires and scales from a theoretical point of view, which need to be further verified in terms of reliability and scientificity. Therefore, this paper conducts a research on the prediction and evolution of distance education learners’ feedback attitudes towards teachers based on deep learning. With the help of deep learning technology, it is easy to discover the advantages of distributed feature representation of data, and ARIMA model is combined with BP neural network model to construct a predictive model of distance education learners' feedback attitudes towards teachers. This paper makes the evolution analysis of distance education learners' feedback attitudes towards teachers, and introduces the assumptions and principles of the evolutionary analysis model in detail. The experimental results verify the effectiveness of the proposed model, and the analysis results of learner attitude evolution based on distance English learning as an example are given.