Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph representation learning scenarios. However, when applied to graph data in real world, GNNs have encountered scalability issues. Existing GNNs often have high computational load in both training and inference stages, making them incapable of meeting the performance needs of large-scale scenarios with a large number of nodes. Although several studies on scalable GNNs have developed, they either merely improve GNNs with limited scalability or come at the expense of reduced effectiveness. Inspired by knowledge distillation's (KDs) achievement in preserving performances while balancing scalability in computer vision and natural language processing, we propose an enhanced scalable GNN via KD (KD-SGNN) to improve the scalability and effectiveness of GNNs. On the one hand, KD-SGNN adopts the idea of decoupled GNNs, which decouples feature transformation and feature propagation in GNNs and leverages preprocessing techniques to improve the scalability of GNNs. On the other hand, KD-SGNN proposes two KD mechanisms (i.e., soft-target (ST) distillation and shallow imitation (SI) distillation) to improve the expressiveness. The scalability and effectiveness of KD-SGNN are evaluated on multiple real datasets. Besides, the effectiveness of the proposed KD mechanisms is also verified through comprehensive analyses.