I understand how gradient descent works. But I have trouble understanding why we usually use the derivative in the equation.

The equation is:
new\_value = old\_value - learning\_rate \* **derivative**

I understand the derivative tells which direction to go. But why do we use learning\_rate \* **derivative** to find the new\_value? What if I use learning\_rate \* **a constant, e.g., 1**

I mean I can do this:

If the derivative is negative, new\_value = old\_value **plus** learning\_rate \* **1**

If the derivative is positive, new\_value = old\_value **minus** learning\_rate \* **1**

I know this is awkward. I just want to say there might exist many ways. Why should we use learning\_rate \* **derivative**?

Your insight is highly appreciated!