Corpus-Driven Resource Recommendation Algorithm for English Online Autonomous Learning.

One of the most significant aspects of English teaching, as well as the embodiment of students' comprehensive English skill, is the cultivation of English learning ability. Teachers of English should help students understand the topic's material and be able to convey, describe, and analyze the topic's substance, such as summarizing, subjective judgments analysis, and tale continuation. Students' English learning is restricted by the learning environment, teachers' quality, students' own ability, and other aspects. Furthermore, schools and families do not prioritize English learning, resulting in low teacher expectations for English instruction, as well as a lack of English practice and strategy training among students. In order to improve the inefficiency of English teaching, this paper combines corpus technology with English teaching and proposes an online autonomous learning resource recommendation algorithm. The model is optimized in the aspects of high efficiency, diversity, and timeliness of learning resource recommendation supported by deep learning technology. The model is pretrained through the processed dataset, and the algorithm designed in this study is compared with the classical algorithm to verify the rationality and effectiveness of the algorithm designed in this study. Based on the previous studies, this study attempts to apply the teaching model combining corpus and recommendation algorithm to online teaching, so as to optimize English teaching model and teaching methods.