  Knowledge tracing is a sequence prediction problem where the goal is to
predict the outcomes of students over questions as they are interacting with a
learning platform. By tracking the evolution of the knowledge of some student,
one can optimize instruction. Existing methods are either based on temporal
latent variable models, or factor analysis with temporal features. We here show
that factorization machines (FMs), a model for regression or classification,
encompasses several existing models in the educational literature as special
cases, notably additive factor model, performance factor model, and
multidimensional item response theory. We show, using several real datasets of
tens of thousands of users and items, that FMs can estimate student knowledge
accurately and fast even when student data is sparsely observed, and handle
side information such as multiple knowledge components and number of attempts
at item or skill level. Our approach allows to fit student models of higher
dimension than existing models, and provides a testbed to try new combinations
of features in order to improve existing models.
