A Personalized Course Content Pushing Method Based on Machine Learning for Online Teaching of English Translation

A personalized course content delivery method based on machine learning for online teaching of English translation is studied. The difficulties and challenges faced in English education are introduced, pointing out that there are differences and diversity in levels, interests and learning styles, etc.; machine learning algorithms are used to deeply analyze data in students’ personal files, including their learning history records, course grades and personal information, etc., to discover students’ learning levels, learning interests and learning characteristics, etc. Given a training sample of course standards, an interest model, solving the similarity among users using the similarity of clouds, acquiring the nearest neighbors of target users, and generating the pushed course contents and learning materials. The experimental results show that the results of personalized course content pushing for online teaching of English translation meet the real needs and have good application effects.