Personalized Online Training Method for Power Dispatchers Based on Machine Learning

To fully utilize online course resources and optimize the job training for power dispatchers, this paper proposes a personalized learning path recommendation algorithm based on machine learning. The paper employs the Deep Q-learning algorithm to construct a recommendation model aimed at suggesting a series of knowledge points to learners, with the ultimate goal of improving learning efficiency. By analyzing personalized data such as the learner's profession, job requirements, and skill level, the process involves job and skill analysis, course module construction, and personalized learning path design. This approach achieves a more efficient and scientific personalized education model, helping learners to achieve their learning goals with minimal cost.