An innovative training model for developing talents in French language education based on the deep learning model

Abstract

Modern society is developing rapidly, new technological means are emerging, people’s mindset is changing day by day, and all aspects of social life have undergone great changes. In such an era, French teaching is facing new challenges, but it is also a new opportunity for teaching reform. The introduction of new deep learning model teaching tools in traditional teaching, the construction of online education, and the adoption of a combination of online and offline teaching models enable French teaching to adapt to the characteristics and needs of the new era and ensure the quality of teaching. The model uses deep learning as technical support and takes the prediction of students’ answers to a question as a judgment indicator. Through the generation of directed graphs in the model, the model structure is optimized to improve its application in predicting students’ answers compared to the classical model. It is prepared for the next step of building a system for personalized question recommendation using deep reinforcement learning. The model plays a role as a student simulator in the recommender system. So its prediction effect reflects the simulation effect on students. Finally, it is experimentally demonstrated that the proposed deep knowledge tracking model based on directed graphs has a significant improvement in prediction effectiveness compared to the traditional model.