Yes this is True. I think it's clearer now. I was thinking link actually transforms the 0/1 but it doesn't it works on the aggregated outcomes (which is percent passed failed). Is this correct?

No - it doesn't do anything to the data itself. It models the relationship between the data and the mean. You don't transform the predictors.

For logistic regression you're assuming that

\(Y_i \sim Bin(n_i, p_i)\)

which says that the response has a binomial distribution with parameters \(n_i\) (the number of observations/students observed for this response) and \(p_i\) (the success probability for each observation/student).

That seems simple enough but the logistic regression part adds the assumption that we can additionally model the \(p_i\) as a function of the covariates. This is what allows us to think things like "the success probability increases as the covariates increase". How we actually 'link' the \(p_i\) with the covariates depends on ... you guessed it - the link function. For logistic regression we assume

\(log(\frac{p_i}{1-p_i}) = \beta_0 + \beta_1x_i\)

So we are saying that if we apply the link function to \(p_i\) we get a linear function with respect to the covariates. Notice we don't apply the link function to the covariates - we apply it to \(p_i\).