Backpropagation on a convolutional layer

Online tutorials describe in depth the convolution of an image with a filter, etc; However, I have not seen one that describes the backpropagation on the filter (at least visually). First let me try to explain how I understand backpropagation on a fully connected network. The last partial derivative is the most interesting one in this case ... and it is equal to the value of the first input (Single value). The original question was how does one perform backpropagation on a convolutional layer - for example $$\frac{\partial Error}{\partial W_1}?$$ The convolutional layer as described online.