Meta Learning Based Teacher Collaborative Innovation Models Research on College English Courses under Global Competence Framework

In the context of rapid advances in educational technology, improved collaboration between teachers is essential to meet the challenges of dynamic task allocation and resource optimization in higher education. Traditional collaboration methods are often inefficient and lack the adaptability required for complex teaching scenarios. To address these issues, this study proposes a new model for optimizing teacher collaboration that integrates Model-Agnostic Meta-Learning (MAML) and Graph Neural Networks (GNNs). The hybrid framework exploits the rapid task adaptation capability of MAML and the advantages of GNNs in dynamic network modelling to optimize task allocation and improve collaboration efficiency. The model is evaluated using a dataset from a university English course, including teacher collaboration networks, task attributes and student feedback. Experimental results show significant improvements in key metrics. The proposed method resulted in a 35% increase in collaboration efficiency, a 92% increase in task completion rate, and a student feedback score of 4.5 out of 5, outperforming both the traditional method and the non-optimized GNNs model. The results highlight the effectiveness of combining meta-learning and graph neural networks in solving dynamic collaboration challenges.