Intelligent Biotechnology English Teaching Mode Based on Deep Learning

Dan Zhou
Basic Teaching Department, Liaoning Finance Vocational College, Shenyang 110122, China

Abstract:

As a field with an extremely high degree of globalization, biotechnology requires practitioners to have strong English proficiency. Academic papers, international conferences, and cross-border cooperation all require English as the main language of communication. However, the traditional teaching model is difficult to meet the precise needs of biotechnology majors for professional English, especially in aspects such as academic writing, understanding of professional terminology, and oral expression. Compared with traditional teaching mode, online English teaching mode has greatly improved English learning efficiency.  In this paper, the CF (collaborative filtering) method combined with Doc2vec is proposed. Firstly, the course text is converted into document vector; Then, in the data resources, the course-document vector is used to replace the user-course matrix, and the document vector is used as the course feature to calculate the similarity. Finally, the CF recommendation algorithm is used to recommend the appropriate course for users. The experimental statistics show that the classification accuracy of DL algorithm reaches 93.238%, and the accuracy is still as high as 91.919% even if the data volume is relatively large and there are many types of data. This model is helpful to predict learners' interests, and can further screen recommended learning resources to improve learners' online learning experience.