Quick Intro to Perceptrons

Generally, single simple perceptrons work like this:

i1＼w1
    θ - output
i2／w2

Where:
i1 and i2 are inputs
w1 and w2 are weights
θ is a threshold or activation function

A formula to sum this up would be:
output = θ(Σ(i*w))

Ergo:
The result of the activation function evaluating the value of the summation of all input i*weight i

The activation function is a nonlinear function that outputs a different value given two different ranges of values. For example:
If the value of Σ(i*w) < 1 then θ(Σ(i*w)) = 0
Else, if the value of Σ(i*w) >= 1 then θ(Σ(i*w)) = 1
Note that this is the usual activation function - referred to as a "Step function", but if you'd rather use a different function, make sure to modify your weights accordingly>

A typical neural network only has binary inputs and outputs. Ergo, values of 0 or 1 only.
Weights typically range from a value from 0 to 1.

HOWEVER.

Your problem seems to use non-binary input values (i1 = proportion of exercises solved; i2 = proportion of lectures visited)
The partially-shaded square figure is essentially a graphic representation of the perceptron; the shaded area represents an output of "1" (test passed) while the non-shaded area is an output of "0" (failed).
Not sure what the problem is asking for (inputs? weights? etc.) but generally you need to give the range of values for the inputs, weights, and the theta (threshold/activation function) to mathematically define a perceptron.
That's basically how it goes; tried to keep it as un-obvious as possible.
Feel free to ask if you need some more help.