Graph Neural Networks (GNNs) have achieved great success in many applications, and mini-batch training has become the de facto standard for training GNNs on large-scale graphs. When developing mini-batch GNN training systems on CPU-GPU platforms, existing dedicated batching systems adopt a static workload-processor binding strategy, where the costly mini-batch preparation workload is exclusively assigned to either the CPU or the GPU. This leads to low utilization of CPU cores, PCIe bandwidth, and GPU computing capability, resulting in suboptimal training efficiency. To address this problem, we develop MorphGL, a novel GNN training system featuring a collective batching design. MorphGL adaptively dispatches the mini-batch preparation workload to both the CPU and GPU, ensuring that the workload distribution aligns with the CPU-GPU setup of the running machine for optimal efficiency. To maximize resource utilization, MorphGL employs the Dual-Buffer Scheduling algorithm to collectively schedule training stages across the CPU, PCIe, and GPU. Extensive experiments on three large real-world graphs with billions of edges and four machines with representative CPU-GPU configurations demonstrate that MorphGL consistently outperforms state-of-the-art GNN training systems, achieving up to 2.76x and 2.2x speedup over SALIENT and DUCATI, respectively.