  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.
