You can call pca() to get the loadings and scores for each of the 39 different features for each PC. Like the first column represents PC1 and the 39 different values in the loadings vector represent the weights of the 39 different original feature values. You can also ask pca() for the amount of output variation explained by each of the original feature, like feature 1 (score) explains 60% of the variation, and feature 2 (performance metric 2, like days of class missed or whatever) explains 30% of the variation.
I'm not sure why you're doing kmeans on PCs in the first place. Seems weird to me. I mean all the PC's are supposed to be independent so plotting any of them vs the other would just look like a random shotgun blast, kind of like yours does. There is only very weak correlation, as expected. So why do clustering on them? If anything you'd do kmeans on the original data, not the principal components.
