  Knowledge tracing refers to a family of methods that estimate each student's
knowledge component/skill mastery level from their past responses to questions.
One key limitation of most existing knowledge tracing methods is that they can
only estimate an \emph{overall} knowledge level of a student per knowledge
component/skill since they analyze only the (usually binary-valued) correctness
of student responses. Therefore, it is hard to use them to diagnose specific
student errors. In this paper, we extend existing knowledge tracing methods
beyond correctness prediction to the task of predicting the exact option
students select in multiple choice questions. We quantitatively evaluate the
performance of our option tracing methods on two large-scale student response
datasets. We also qualitatively evaluate their ability in identifying common
student errors in the form of clusters of incorrect options across different
questions that correspond to the same error.
