Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
This work provides a flexible and efficient method for optimizing instruction in educational platforms, though it is incremental as it builds on existing factorization machine techniques.
The paper tackles knowledge tracing by applying factorization machines to predict student performance, showing that this approach accurately estimates student knowledge from sparse data and handles side information like multiple knowledge components.
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.