The for addition/subtraction uncertainties add in quadrature. Therefore the combined confidence interval is:
$$ u_c = \sqrt{u_m^2+u_r^2+u_w^2}$$
For an individual test the confidence interval can be given by the standard deviation of the mean.
$$ s_m = \frac{s}{\sqrt{n}}$$
where $s$ is the standard deviation of the sample. This should then be multiplied by the coverage factor to get the desired confidence interval.
$$ u=ks_m$$
For a normal distribution at 95% confidence $k=1.96$ ($\simeq2$).
In matlab the easiest way to do it is to just calculate standard deviation (std()) or variance and go from there. I don't use Stata/SAS so don't know if they have any specific functions.
I would also think carefully about the meaning of your test. You should probably normalise the scores so they are all on the same scale. Additionally I suspect they may be some correlation between results for a single student taking all three tests. That is students are likely to good/bad in all the tests. If this is the case you would get a larger confidence interval if you take a sample of different students total score than for the total of different samples on each test (what you currently do).
(Aside on the general case)
In general for any function $f(x_1, x_2, ... , x_m)$ where all $x_i$ are independent of each other and have associated uncertainty, $u_i$, the combined uncertainty $u_c$ can be given by:
$$ u_c^2 = \sum_{i=1}^m\left(\frac{\partial f}{\partial x_i}^2\right)u_i^2$$
You should make sure all $u_i$ have the same coverage factor. You can calculate uncertainty $k=1$ and then expand the combined uncertainty if you need to.
