Paradigms like IRT use measurement equations to model student behavior probabilistically. We investigate an analogous LLM-based approach, the noisy channel model, to compute and compare the conditional likelihoods of student writing and produce auditable token-level inferences about student skills. We compare against other LLM-based methods on accuracy, calibration, and interpretability.