Reinforcement Learning Based Collaborative Innovation Mechanism Research on Teacher Professional Development in College English Courses

Education is now undergoing a digital transformation. Integrating artificial intelligence, especially deep reinforcement learning, into teaching has gradually become a relatively new direction to solve the problems of optimizing teaching strategies and the professional development of teachers. The current mainstream teaching model actually has great limitations in dealing with the complexity of dynamic classrooms and the needs of personalized learning. We conducted a survey of front-line teachers, and about 67% of the interviewed teachers said that traditional teaching methods are difficult to respond in real time when faced with emergencies in the classroom. So, in order to solve this problem, we tried to develop an intelligent teaching optimization system, the core of which is based on Deep Q-Network, that is, DQN algorithm, and we also added a module to support teacher collaborative innovation. The experimental scenario is the English course data of a certain university. On this basis, we built a dynamic optimization model with a three-layer neural network architecture. Our algorithm design is actually optimized specifically for three key issues: first, through the Q-learning framework, the teaching strategy has the ability to self-evolve; second, the dual network structure is used to reduce the deviation in numerical estimation; third, the experience replay mechanism is introduced to improve the stability and robustness of the entire decision. The effects of these technical improvements are also very obvious. Compared with traditional algorithms, the convergence speed of our target network DQN has increased by about 40%, and the iteration cycle of the entire teaching strategy has been shortened to about one-third of the original. Then from the experimental results, the effect is quite breakthrough. The number of classroom interactions per week in the experimental group increased from an average of 12 times to 16.2 times, and the student participation index also increased by 21 percentage points. Another point worth mentioning is that in the area of cross-disciplinary lesson preparation, our collaborative decision-making module has increased the efficiency by 30%, and the reuse rate of teaching resources has increased from 58% to 82%.