Deep Factorization Machines for Knowledge Tracing

Abstract

This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.

arXiv comment4 pages, 1 table, accepted at the 13th BEA workshop, co-located with NAACL HLT 2018 conference in New Orleans on June 5, 2018