Application of artificial intelligence graph convolutional network in classroom grade evaluation

The traditional classroom grade assessment method has some problems, such as strong subjectivity and single dimension, and it is difficult to fully reflect students' real learning state. This study proposes a classroom performance evaluation model based on Graph Convolutional Network (GCN). By constructing an interaction relationship graph among students, this study applies Graph Neural Network (GNN) to educational data analysis to enhance the objectivity and accuracy of evaluation. The study collects multi-source data from teaching management systems, classroom observation records, and online learning platforms. It constructs a graph structure containing students' individual attributes and social relationships, and designs a GCN model architecture and training process suitable for educational scenarios. The experimental results show that the model has achieved significantly better performance than traditional machine learning methods in the four-class classroom performance prediction task. Through ablation experiments and comparative analysis of different graph construction strategies, the important role of social relationship information in student performance prediction is verified. This study not only expands the application path of GNN in the field of educational assessment but also provides new technical ideas for realizing an intelligent and dynamic classroom grade assessment system.