Abstract: With the development of social economy, science and technology, and the popularity of mobile Internet, human society has gradually entered the information age. The rapid growth and disorderly distribution of information have posed challenges to English learners who conduct independent and individualized learning anytime and anywhere. Reading plays an important role in language learning. Traditionally, it is difficult for the English reading method based on chapters to meet the fragmented learning needs of learners who are often in a mobile environment. Through the literature research, this paper designed the five-dimensional feature model of learners and the three-dimensional feature model of English reading resources in the fragmented learning environment. Combined with the machine learning algorithm ID3, the decision tree classification model of English fragmented reading was constructed to push resources, adapt to the individual needs of the learners, and then specifically enhance the different aspects of the learner's reading ability, thereby improving the pass rate of the College English Test-Band Four.

Key words: adaptive push, ID3 algorithm, learner model, English reading, fragmented learning