  We use both Bayesian and neural models to dissect a data set of Chinese
learners' pre- and post-interventional responses to two tests measuring their
understanding of English prepositions. The results mostly replicate previous
findings from frequentist analyses and newly reveal crucial interactions
between student ability, task type, and stimulus sentence. Given the sparsity
of the data as well as high diversity among learners, the Bayesian method
proves most useful; but we also see potential in using language model
probabilities as predictors of grammaticality and learnability.
