A Study of English Informative Teaching Strategies Based on Deep Learning

Abstract

The rapid development of artificial intelligence brings new development opportunities and challenges to English teaching university. This paper explores the concept of “smart education” and the path of building an ecological information-based teaching model of English college by interpreting the concepts of artificial intelligence, deep learning, ecological linguistics, and language education. Artificial intelligence, especially deep learning, will be promising in many aspects, such as the analysis of individual differences of language learners, customized learning content, diversified and three-dimensional teaching media, the role of teachers as smart classroom designers, and multidimensional and dynamic formative assessments. By relying on the data mining technology of deep learning to analyze learners’ characteristics, the smart classroom design, the promotion of language learners’ independent learning, and the establishment of dynamic and complete learner profiles, the language learning process is no longer a linear process but an evolving open loop, ultimately forming a harmonious development of various ecological niches in the language learning process. In this paper, we study and design a deep learning-based English informatics teaching system to develop a deep learning-based scoring prediction model. The model incorporates deep learning models based on word embedding and text convolutional networks, which can uncover the hidden interest features of academics for English. The experimental research results prove that the online e-learning service platform cannot only effectively meet the diverse and personalized English learning needs of university students, but also improve the learning efficiency of teachers and students.