Jill-Jênn Vie, RIKEN AIP (Tokyo)
21 December 2018, 10:30-11:30
ENS, S16
Knowledge Tracing Machines – Factorization Machines for Knowledge Tracing
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 provide feedback, and optimize instruction accordingly. Existing methods are either based on temporal latent variable models like deep knowledge tracing, or factor analysis like item response theory. We present factorization machines (FMs), a model for regression or classification that encompasses several existing models in the educational data mining 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. We will open up to other applications in crowdsourcing and preference elicitation. To reproduce our experiments, a tutorial is available on: https://github.com/jilljenn/ktm. The article is on arXiv: https://arxiv.org/abs/1811.03388
