As the backbone of future basic education, the independent learning ability of English teacher trainees directly affects the quality of teaching and student development. However, the existing cultivation mode lacks personalized guidance, and it is difficult to accurately identify the learning state, resulting in poor cultivation effect. Focusing on the cultivation needs of independent learning ability of English teacher training students, this study constructs a deep knowledge tracking algorithm based on students’ state (DKT-ST) and an open learner model (ALS-OLM) for independent learning services. The model adopts a two-layer memory network structure, introduces the forgetting factor and attention mechanism, stores knowledge concept information through a static matrix, and updates students’ knowledge mastery through a dynamic matrix to realize accurate tracking of learning status. Experiments on three public datasets show that the AUC value of the DKT-ST model on the ASSISTments2009 dataset reaches 0.817, which is a 5.42% improvement compared with the DKT-DSC model, and on the KDDCup2010 dataset is a 2.21% improvement. The results of the teaching experiment show that the average improvement of students’ independent learning ability in the experimental class is 25.61 points, with an increase of 37.20%, which is significantly better than that of the control class, which is 14.89%. The analysis of students’ ability attributes shows that the key ability dimension is improved by 0.46. The study proves that the personalized learning model based on artificial intelligence algorithms can effectively improve the independent learning ability of English teacher training students, and provides technical paths and practical references for the reform of teacher education.
