I can't answer the question about whether or not the students will be disadvantaged in the competition by not using categorical variables, but I can explain what R is doing with categorical variables in multiple regression. I think...
When you add a categorical variable to your model, it converts the levels of that variable to variables in their own right, coded as binary, 0 means doesn't fit that category, 1 means fits that category. Then it adds those variables to the regression as quantitative variables.
For example, if you chose to include hbcu (historically Black colleges and universities), R would create variables hbcuNo and hbcuYes. Morehouse College would have 0 added to its case under hbcuNo and 1 added to its case under hbcuYes, while Harvard would have 1 added to its case under hbcuNo and 0 added to its case under hbcuYes.
This, of course, would get wild if you tried to include the college name as a predictor!
You can see what it does by adding a categorical variables to my_model
then running summary(my_model)
to have a look.