What throws me off is the NULL Hypothesis. According to my calculation
using EXCEL its 7.37481E-27 or 0.00000000000000000000000000737 (for a
paired, 2 tail TTEST). Is this realistic to get?
It depends on the number of students and difference between pre and post. I think there is probably a problem with your calculation however.
My understanding is that you have to achieve <.05 to reject the null
hypothesis. And my calculated TTESTS are really small.
The number .05 is arbitrary and based on an example Ronald Fisher used ~80 years ago. He later said that no one should use the same number for every case, it depends on the circumstances. Your p-value is very small so I am sure would be considered "significant" by anyone, however there is probably a problem with your calculation.
Does this mean
I can reject the null hypothesis and say that what I did to increase
reading scores was effective?
Rejecting the null hypothesis only means that the pre and post scores were not exactly the same. It could also be significant if the students were worse in later semester. Or the scores could be different for some other reason besides your teaching (a popular tv show ended so they studied more, it could be anything).
It is best to plot your results to show the effect. Plot a line for each student between pre and post. Did some students improve while others did not, or even got worse? Perhaps there is something in common regarding who got better and who did not (if this is the case) that could help you improve your teaching strategy even further. Most likely you do not only care about the "average student" but more how to help each individual student.
Also, some students who took a test in fall withdrew from my class
mid-year and didn’t take a spring test. Do I leave those cells blank
or do I fill them in with zeros? Or do I just pretend that those kids
never existed? How much would that throw off the results? Thanks.
Do not fill them in with zeros. It would probably be easiest for you to drop them from the significance test analysis (pretend they didnt exist). But you should not ignore the data! For example, if there was a large difference pre and post for all the other students and the ones who dropped the course were the ones with high scores the first semester this may skew your results. Perhaps they withdrew because of the teaching method, etc. There is no statistical test for this, you just have to think about how to explain what happened to generate the data you got.
If you post the calculation you did I can help further, still I think it is more important to plot the data and look at the effect for each student rather than calculate a p-value.