Acceso usuarios
Chen Jun, Jiang Man
This article studies the integration strategy of training and integrating algorithms for study travel instructors based on professional ability enhancement. With the continuous innovation of educational models, study tours, as an emerging educational method, are of great significance in cultivating students’ comprehensive qualities. The professional ability of a research tour instructor is a key factor in determining the quality of their teaching. Therefore, this article aims to enhance the professional ability of research travel instructors by integrating algorithm strategies, and thereby optimize the educational effectiveness of research travel. Firstly, this article analyzes the current situation and problems in the training of research travel instructors, including an imperfect training system and a disconnect between training content and actual needs. In response to these issues, a training strategy for graduate travel mentors based on vocational ability enhancement has been proposed. Combining modern educational technology and big data analysis methods, a training model for graduate travel mentors based on fusion algorithms has been constructed. This model integrates multiple algorithms including particle swarm optimization and support vector machine, and provides personalized learning resources and training suggestions for mentors through data mining and analysis. At the same time, the model also introduces a real-time feedback mechanism, allowing mentors to adjust and improve in a timely manner during the learning process.
